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Multiple Instance Learning (MIL) is a sub-domain of classification problems
with positive and negative labels and a "bag" of inputs, where the label is
positive if and only if a positive element is contained within the bag, and
otherwise is negative. Training in this context requires associating the
bag-wide label to instance-level information, and implicitly contains a causal
assumption and asymmetry to the task (i.e., you can't swap the labels without
changing the semantics). MIL problems occur in healthcare (one malignant cell
indicates cancer), cyber security (one malicious executable makes an infected
computer), and many other tasks. In this work, we examine five of the most
prominent deep-MIL models and find that none of them respects the standard MIL
assumption. They are able to learn anti-correlated instances, i.e., defaulting
to "positive" labels until seeing a negative counter-example, which should not
be possible for a correct MIL model. We suspect that enhancements and other
works derived from these models will share the same issue. In any context in
which these models are being used, this creates the potential for learning
incorrect models, which creates risk of operational failure. We identify and
demonstrate this problem via a proposed "algorithmic unit test", where we
create synthetic datasets that can be solved by a MIL respecting model, and
which clearly reveal learning that violates MIL assumptions. The five evaluated
methods each fail one or more of these tests. This provides a model-agnostic
way to identify violations of modeling assumptions, which we hope will be
useful for future development and evaluation of MIL models. | [
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] |
Generalist robot manipulators need to learn a wide variety of manipulation
skills across diverse environments. Current robot training pipelines rely on
humans to provide kinesthetic demonstrations or to program simulation
environments and to code up reward functions for reinforcement learning. Such
human involvement is an important bottleneck towards scaling up robot learning
across diverse tasks and environments. We propose Generation to Simulation
(Gen2Sim), a method for scaling up robot skill learning in simulation by
automating generation of 3D assets, task descriptions, task decompositions and
reward functions using large pre-trained generative models of language and
vision. We generate 3D assets for simulation by lifting open-world 2D
object-centric images to 3D using image diffusion models and querying LLMs to
determine plausible physics parameters. Given URDF files of generated and
human-developed assets, we chain-of-thought prompt LLMs to map these to
relevant task descriptions, temporal decompositions, and corresponding python
reward functions for reinforcement learning. We show Gen2Sim succeeds in
learning policies for diverse long horizon tasks, where reinforcement learning
with non temporally decomposed reward functions fails. Gen2Sim provides a
viable path for scaling up reinforcement learning for robot manipulators in
simulation, both by diversifying and expanding task and environment
development, and by facilitating the discovery of reinforcement-learned
behaviors through temporal task decomposition in RL. Our work contributes
hundreds of simulated assets, tasks and demonstrations, taking a step towards
fully autonomous robotic manipulation skill acquisition in simulation. | [
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] |
Digital mental health (DMH) interventions, such as text-message-based lessons
and activities, offer immense potential for accessible mental health support.
While these interventions can be effective, real-world experimental testing can
further enhance their design and impact. Adaptive experimentation, utilizing
algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB)
problems, can lead to continuous improvement and personalization. However, it
remains unclear when these algorithms can simultaneously increase user
experience rewards and facilitate appropriate data collection for
social-behavioral scientists to analyze with sufficient statistical confidence.
Although a growing body of research addresses the practical and statistical
aspects of MAB and other adaptive algorithms, further exploration is needed to
assess their impact across diverse real-world contexts. This paper presents a
software system developed over two years that allows text-messaging
intervention components to be adapted using bandit and other algorithms while
collecting data for side-by-side comparison with traditional uniform random
non-adaptive experiments. We evaluate the system by deploying a
text-message-based DMH intervention to 1100 users, recruited through a large
mental health non-profit organization, and share the path forward for deploying
this system at scale. This system not only enables applications in mental
health but could also serve as a model testbed for adaptive experimentation
algorithms in other domains. | [
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] |
Today, deep learning optimization is primarily driven by research focused on
achieving high inference accuracy and reducing latency. However, the energy
efficiency aspect is often overlooked, possibly due to a lack of sustainability
mindset in the field and the absence of a holistic energy dataset. In this
paper, we conduct a threefold study, including energy measurement, prediction,
and efficiency scoring, with an objective to foster transparency in power and
energy consumption within deep learning across various edge devices. Firstly,
we present a detailed, first-of-its-kind measurement study that uncovers the
energy consumption characteristics of on-device deep learning. This study
results in the creation of three extensive energy datasets for edge devices,
covering a wide range of kernels, state-of-the-art DNN models, and popular AI
applications. Secondly, we design and implement the first kernel-level energy
predictors for edge devices based on our kernel-level energy dataset.
Evaluation results demonstrate the ability of our predictors to provide
consistent and accurate energy estimations on unseen DNN models. Lastly, we
introduce two scoring metrics, PCS and IECS, developed to convert complex power
and energy consumption data of an edge device into an easily understandable
manner for edge device end-users. We hope our work can help shift the mindset
of both end-users and the research community towards sustainability in edge
computing, a principle that drives our research. Find data, code, and more
up-to-date information at https://amai-gsu.github.io/DeepEn2023. | [
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] |
Machine Unlearning (MU) algorithms have become increasingly critical due to
the imperative adherence to data privacy regulations. The primary objective of
MU is to erase the influence of specific data samples on a given model without
the need to retrain it from scratch. Accordingly, existing methods focus on
maximizing user privacy protection. However, there are different degrees of
privacy regulations for each real-world web-based application. Exploring the
full spectrum of trade-offs between privacy, model utility, and runtime
efficiency is critical for practical unlearning scenarios. Furthermore,
designing the MU algorithm with simple control of the aforementioned trade-off
is desirable but challenging due to the inherent complex interaction. To
address the challenges, we present Controllable Machine Unlearning (ConMU), a
novel framework designed to facilitate the calibration of MU. The ConMU
framework contains three integral modules: an important data selection module
that reconciles the runtime efficiency and model generalization, a progressive
Gaussian mechanism module that balances privacy and model generalization, and
an unlearning proxy that controls the trade-offs between privacy and runtime
efficiency. Comprehensive experiments on various benchmark datasets have
demonstrated the robust adaptability of our control mechanism and its
superiority over established unlearning methods. ConMU explores the full
spectrum of the Privacy-Utility-Efficiency trade-off and allows practitioners
to account for different real-world regulations. Source code available at:
https://github.com/guangyaodou/ConMU. | [
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] |
The difficulty of identifying the physical model of complex systems has led
to exploring methods that do not rely on such complex modeling of the systems.
Deep reinforcement learning has been the pioneer for solving this problem
without the need for relying on the physical model of complex systems by just
interacting with it. However, it uses a black-box learning approach that makes
it difficult to be applied within real-world and safety-critical systems
without providing explanations of the actions derived by the model.
Furthermore, an open research question in deep reinforcement learning is how to
focus the policy learning of critical decisions within a sparse domain. This
paper proposes a novel approach for the use of deep reinforcement learning in
safety-critical systems. It combines the advantages of probabilistic modeling
and reinforcement learning with the added benefits of interpretability and
works in collaboration and synchronization with conventional decision-making
strategies. The BC-SRLA is activated in specific situations which are
identified autonomously through the fused information of probabilistic model
and reinforcement learning, such as abnormal conditions or when the system is
near-to-failure. Further, it is initialized with a baseline policy using policy
cloning to allow minimum interactions with the environment to address the
challenges associated with using RL in safety-critical industries. The
effectiveness of the BC-SRLA is demonstrated through a case study in
maintenance applied to turbofan engines, where it shows superior performance to
the prior art and other baselines. | [
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] |
Deep policy networks enable robots to learn behaviors to solve various
real-world complex tasks in an end-to-end fashion. However, they lack
transparency to provide the reasons of actions. Thus, such a black-box model
often results in low reliability and disruptive actions during the deployment
of the robot in practice. To enhance its transparency, it is important to
explain robot behaviors by considering the extent to which each input feature
contributes to determining a given action. In this paper, we present an
explicit analysis of deep policy models through input attribution methods to
explain how and to what extent each input feature affects the decisions of the
robot policy models. To this end, we present two methods for applying input
attribution methods to robot policy networks: (1) we measure the importance
factor of each joint torque to reflect the influence of the motor torque on the
end-effector movement, and (2) we modify a relevance propagation method to
handle negative inputs and outputs in deep policy networks properly. To the
best of our knowledge, this is the first report to identify the dynamic changes
of input attributions of multi-modal sensor inputs in deep policy networks
online for robotic manipulation. | [
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] |
In the past five years, the use of generative and foundational AI systems has
greatly improved the decoding of brain activity. Visual perception, in
particular, can now be decoded from functional Magnetic Resonance Imaging
(fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers
from a limited temporal resolution ($\approx$0.5 Hz) and thus fundamentally
constrains its real-time usage. Here, we propose an alternative approach based
on magnetoencephalography (MEG), a neuroimaging device capable of measuring
brain activity with high temporal resolution ($\approx$5,000 Hz). For this, we
develop an MEG decoding model trained with both contrastive and regression
objectives and consisting of three modules: i) pretrained embeddings obtained
from the image, ii) an MEG module trained end-to-end and iii) a pretrained
image generator. Our results are threefold: Firstly, our MEG decoder shows a 7X
improvement of image-retrieval over classic linear decoders. Second, late brain
responses to images are best decoded with DINOv2, a recent foundational image
model. Third, image retrievals and generations both suggest that MEG signals
primarily contain high-level visual features, whereas the same approach applied
to 7T fMRI also recovers low-level features. Overall, these results provide an
important step towards the decoding - in real time - of the visual processes
continuously unfolding within the human brain. | [
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] |
Objectives: Artificial intelligence (AI) applications utilizing electronic
health records (EHRs) have gained popularity, but they also introduce various
types of bias. This study aims to systematically review the literature that
address bias in AI research utilizing EHR data. Methods: A systematic review
was conducted following the Preferred Reporting Items for Systematic Reviews
and Meta-analyses (PRISMA) guideline. We retrieved articles published between
January 1, 2010, and October 31, 2022, from PubMed, Web of Science, and the
Institute of Electrical and Electronics Engineers. We defined six major types
of bias and summarized the existing approaches in bias handling. Results: Out
of the 252 retrieved articles, 20 met the inclusion criteria for the final
review. Five out of six bias were covered in this review: eight studies
analyzed selection bias; six on implicit bias; five on confounding bias; four
on measurement bias; two on algorithmic bias. For bias handling approaches, ten
studies identified bias during model development, while seventeen presented
methods to mitigate the bias. Discussion: Bias may infiltrate the AI
application development process at various stages. Although this review
discusses methods for addressing bias at different development stages, there is
room for implementing additional effective approaches. Conclusion: Despite
growing attention to bias in healthcare AI, research using EHR data on this
topic is still limited. Detecting and mitigating AI bias with EHR data
continues to pose challenges. Further research is needed to raise a
standardized method that is generalizable and interpretable to detect, mitigate
and evaluate bias in medical AI. | [
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] |
This paper proposes a novel contrastive learning framework, called FOCAL, for
extracting comprehensive features from multimodal time-series sensing signals
through self-supervised training. Existing multimodal contrastive frameworks
mostly rely on the shared information between sensory modalities, but do not
explicitly consider the exclusive modality information that could be critical
to understanding the underlying sensing physics. Besides, contrastive
frameworks for time series have not handled the temporal information locality
appropriately. FOCAL solves these challenges by making the following
contributions: First, given multimodal time series, it encodes each modality
into a factorized latent space consisting of shared features and private
features that are orthogonal to each other. The shared space emphasizes feature
patterns consistent across sensory modalities through a modal-matching
objective. In contrast, the private space extracts modality-exclusive
information through a transformation-invariant objective. Second, we propose a
temporal structural constraint for modality features, such that the average
distance between temporally neighboring samples is no larger than that of
temporally distant samples. Extensive evaluations are performed on four
multimodal sensing datasets with two backbone encoders and two classifiers to
demonstrate the superiority of FOCAL. It consistently outperforms the
state-of-the-art baselines in downstream tasks with a clear margin, under
different ratios of available labels. The code and self-collected dataset are
available at https://github.com/tomoyoshki/focal. | [
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] |
Passenger clustering based on trajectory records is essential for
transportation operators. However, existing methods cannot easily cluster the
passengers due to the hierarchical structure of the passenger trip information,
including multiple trips within each passenger and multi-dimensional
information about each trip. Furthermore, existing approaches rely on an
accurate specification of the clustering number to start. Finally, existing
methods do not consider spatial semantic graphs such as geographical proximity
and functional similarity between the locations. In this paper, we propose a
novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can
preserve the hierarchical structure of the multi-dimensional trip information
and cluster them in a unified one-step manner with the ability to determine the
number of clusters automatically. The spatial graphs are utilized in community
detection to link the semantic neighbors. We further propose a tensor version
of Collapsed Gibbs Sampling method with a minimum cluster size requirement. A
case study based on Hong Kong metro passenger data is conducted to demonstrate
the automatic process of cluster amount evolution and better cluster quality
measured by within-cluster compactness and cross-cluster separateness. The code
is available at https://github.com/bonaldli/TensorDPMM-G. | [
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] |
When limited by their own morphologies, humans and some species of animals
have the remarkable ability to use objects from the environment toward
accomplishing otherwise impossible tasks. Robots might similarly unlock a range
of additional capabilities through tool use. Recent techniques for jointly
optimizing morphology and control via deep learning are effective at designing
locomotion agents. But while outputting a single morphology makes sense for
locomotion, manipulation involves a variety of strategies depending on the task
goals at hand. A manipulation agent must be capable of rapidly prototyping
specialized tools for different goals. Therefore, we propose learning a
designer policy, rather than a single design. A designer policy is conditioned
on task information and outputs a tool design that helps solve the task. A
design-conditioned controller policy can then perform manipulation using these
tools. In this work, we take a step towards this goal by introducing a
reinforcement learning framework for jointly learning these policies. Through
simulated manipulation tasks, we show that this framework is more sample
efficient than prior methods in multi-goal or multi-variant settings, can
perform zero-shot interpolation or fine-tuning to tackle previously unseen
goals, and allows tradeoffs between the complexity of design and control
policies under practical constraints. Finally, we deploy our learned policies
onto a real robot. Please see our supplementary video and website at
https://robotic-tool-design.github.io/ for visualizations. | [
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] |
The potential of digital twin technology is yet to be fully realized due to
its diversity and untapped potential. Digital twins enable systems' analysis,
design, optimization, and evolution to be performed digitally or in conjunction
with a cyber-physical approach to improve speed, accuracy, and efficiency over
traditional engineering methods. Industry 4.0, factories of the future, and
digital twins continue to benefit from the technology and provide enhanced
efficiency within existing systems. Due to the lack of information and security
standards associated with the transition to cyber digitization, cybercriminals
have been able to take advantage of the situation. Access to a digital twin of
a product or service is equivalent to threatening the entire collection. There
is a robust interaction between digital twins and artificial intelligence
tools, which leads to strong interaction between these technologies, so it can
be used to improve the cybersecurity of these digital platforms based on their
integration with these technologies. This study aims to investigate the role of
artificial intelligence in providing cybersecurity for digital twin versions of
various industries, as well as the risks associated with these versions. In
addition, this research serves as a road map for researchers and others
interested in cybersecurity and digital security. | [
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] |
Optical tactile sensors have emerged as an effective means to acquire dense
contact information during robotic manipulation. A recently-introduced
`see-through-your-skin' (STS) variant of this type of sensor has both visual
and tactile modes, enabled by leveraging a semi-transparent surface and
controllable lighting. In this work, we investigate the benefits of pairing
visuotactile sensing with imitation learning for contact-rich manipulation
tasks. First, we use tactile force measurements and a novel algorithm during
kinesthetic teaching to yield a force profile that better matches that of the
human demonstrator. Second, we add visual/tactile STS mode switching as a
control policy output, simplifying the application of the sensor. Finally, we
study multiple observation configurations to compare and contrast the value of
visual/tactile data (both with and without mode switching) with visual data
from a wrist-mounted eye-in-hand camera. We perform an extensive series of
experiments on a real robotic manipulator with door-opening and closing tasks,
including over 3,000 real test episodes. Our results highlight the importance
of tactile sensing for imitation learning, both for data collection to allow
force matching, and for policy execution to allow accurate task feedback. | [
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] |
Recent progress in vision language foundation models has shown their ability
to understand multimodal data and resolve complicated vision language tasks,
including robotics manipulation. We seek a straightforward way of making use of
existing vision-language models (VLMs) with simple fine-tuning on robotics
data. To this end, we derive a simple and novel vision-language manipulation
framework, dubbed RoboFlamingo, built upon the open-source VLMs, OpenFlamingo.
Unlike prior works, RoboFlamingo utilizes pre-trained VLMs for single-step
vision-language comprehension, models sequential history information with an
explicit policy head, and is slightly fine-tuned by imitation learning only on
language-conditioned manipulation datasets. Such a decomposition provides
RoboFlamingo the flexibility for open-loop control and deployment on
low-performance platforms. By exceeding the state-of-the-art performance with a
large margin on the tested benchmark, we show RoboFlamingo can be an effective
and competitive alternative to adapt VLMs to robot control. Our extensive
experimental results also reveal several interesting conclusions regarding the
behavior of different pre-trained VLMs on manipulation tasks. We believe
RoboFlamingo has the potential to be a cost-effective and easy-to-use solution
for robotics manipulation, empowering everyone with the ability to fine-tune
their own robotics policy. | [
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] |
Deep learning models have been shown to outperform methods that rely on
summary statistics, like the power spectrum, in extracting information from
complex cosmological data sets. However, due to differences in the subgrid
physics implementation and numerical approximations across different simulation
suites, models trained on data from one cosmological simulation show a drop in
performance when tested on another. Similarly, models trained on any of the
simulations would also likely experience a drop in performance when applied to
observational data. Training on data from two different suites of the CAMELS
hydrodynamic cosmological simulations, we examine the generalization
capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing
GNNs, we capitalize on their capacity to capture structured scale-free
cosmological information from galaxy distributions. Moreover, by including
unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable
our models to extract domain-invariant features. We demonstrate that DA-GNN
achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$
better relative error and up to almost an order of magnitude better $\chi^2$).
Using data visualizations, we show the effects of domain adaptation on proper
latent space data alignment. This shows that DA-GNNs are a promising method for
extracting domain-independent cosmological information, a vital step toward
robust deep learning for real cosmic survey data. | [
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] |
Data sparsity and cold-start problems are persistent challenges in
recommendation systems. Cross-domain recommendation (CDR) is a promising
solution that utilizes knowledge from the source domain to improve the
recommendation performance in the target domain. Previous CDR approaches have
mainly followed the Embedding and Mapping (EMCDR) framework, which involves
learning a mapping function to facilitate knowledge transfer. However, these
approaches necessitate re-engineering and re-training the network structure to
incorporate transferrable knowledge, which can be computationally expensive and
may result in catastrophic forgetting of the original knowledge. In this paper,
we present a scalable and efficient paradigm to address data sparsity and
cold-start issues in CDR, named CDR-Adapter, by decoupling the original
recommendation model from the mapping function, without requiring
re-engineering the network structure. Specifically, CDR-Adapter is a novel
plug-and-play module that employs adapter modules to align feature
representations, allowing for flexible knowledge transfer across different
domains and efficient fine-tuning with minimal training costs. We conducted
extensive experiments on the benchmark dataset, which demonstrated the
effectiveness of our approach over several state-of-the-art CDR approaches. | [
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] |
Publishing and sharing data is crucial for the data mining community,
allowing collaboration and driving open innovation. However, many researchers
cannot release their data due to privacy regulations or fear of leaking
confidential business information. To alleviate such issues, we propose the
Time Series Synthesis Using the Matrix Profile (TSSUMP) method, where
synthesized time series can be released in lieu of the original data. The
TSSUMP method synthesizes time series by preserving similarity join information
(i.e., Matrix Profile) while reducing the correlation between the synthesized
and the original time series. As a result, neither the values for the
individual time steps nor the local patterns (or shapes) from the original data
can be recovered, yet the resulting data can be used for downstream tasks that
data analysts are interested in. We concentrate on similarity joins because
they are one of the most widely applied time series data mining routines across
different data mining tasks. We test our method on a case study of ECG and
gender masking prediction. In this case study, the gender information is not
only removed from the synthesized time series, but the synthesized time series
also preserves enough information from the original time series. As a result,
unmodified data mining tools can obtain near-identical performance on the
synthesized time series as on the original time series. | [
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] |
In today's digitally interconnected world, cybersecurity threats have reached
unprecedented levels, presenting a pressing concern for individuals,
organizations, and governments. This study employs a qualitative research
approach to comprehensively examine the diverse threats of cybersecurity and
their impacts across various sectors. Four primary categories of threats are
identified and analyzed, encompassing malware attacks, social engineering
attacks, network vulnerabilities, and data breaches. The research delves into
the consequences of these threats on individuals, organizations, and society at
large. The findings reveal a range of key emerging threats in cybersecurity,
including advanced persistent threats, ransomware attacks, Internet of Things
(IoT) vulnerabilities, and social engineering exploits. Consequently, it is
evident that emerging cybersecurity threats pose substantial risks to both
organizations and individuals. The sophistication and diversity of these
emerging threats necessitate a multi-layered approach to cybersecurity. This
approach should include robust security measures, comprehensive employee
training, and regular security audits. The implications of these emerging
threats are extensive, with potential consequences such as financial loss,
reputational damage, and compromised personal information. This study
emphasizes the importance of implementing effective measures to mitigate these
threats. It highlights the significance of using strong passwords, encryption
methods, and regularly updating software to bolster cyber defenses. | [
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] |
Generative models of observations under interventions have been a vibrant
topic of interest across machine learning and the sciences in recent years. For
example, in drug discovery, there is a need to model the effects of diverse
interventions on cells in order to characterize unknown biological mechanisms
of action. We propose the Sparse Additive Mechanism Shift Variational
Autoencoder, SAMS-VAE, to combine compositionality, disentanglement, and
interpretability for perturbation models. SAMS-VAE models the latent state of a
perturbed sample as the sum of a local latent variable capturing
sample-specific variation and sparse global variables of latent intervention
effects. Crucially, SAMS-VAE sparsifies these global latent variables for
individual perturbations to identify disentangled, perturbation-specific latent
subspaces that are flexibly composable. We evaluate SAMS-VAE both
quantitatively and qualitatively on a range of tasks using two popular single
cell sequencing datasets. In order to measure perturbation-specific
model-properties, we also introduce a framework for evaluation of perturbation
models based on average treatment effects with links to posterior predictive
checks. SAMS-VAE outperforms comparable models in terms of generalization
across in-distribution and out-of-distribution tasks, including a combinatorial
reasoning task under resource paucity, and yields interpretable latent
structures which correlate strongly to known biological mechanisms. Our results
suggest SAMS-VAE is an interesting addition to the modeling toolkit for machine
learning-driven scientific discovery. | [
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] |
Deep learning-empowered semantic communication is regarded as a promising
candidate for future 6G networks. Although existing semantic communication
systems have achieved superior performance compared to traditional methods, the
end-to-end architecture adopted by most semantic communication systems is
regarded as a black box, leading to the lack of explainability. To tackle this
issue, in this paper, a novel semantic communication system with a shared
knowledge base is proposed for text transmissions. Specifically, a textual
knowledge base constructed by inherently readable sentences is introduced into
our system. With the aid of the shared knowledge base, the proposed system
integrates the message and corresponding knowledge from the shared knowledge
base to obtain the residual information, which enables the system to transmit
fewer symbols without semantic performance degradation. In order to make the
proposed system more reliable, the semantic self-information and the source
entropy are mathematically defined based on the knowledge base. Furthermore,
the knowledge base construction algorithm is developed based on a
similarity-comparison method, in which a pre-configured threshold can be
leveraged to control the size of the knowledge base. Moreover, the simulation
results have demonstrated that the proposed approach outperforms existing
baseline methods in terms of transmitted data size and sentence similarity. | [
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] |
Code Large Language Models (CodeLLMs) have ushered in a new era of code
generation advancements. However, selecting the best solutions from among all
possible CodeLLM solutions remains a challenge. Previous methods frequently
overlooked the intricate functional similarities and interactions between
clusters, resulting in suboptimal results. In this work, we introduce
\textit{SRank}, a novel reranking strategy for selecting the best solution from
code generation that focuses on modeling inter-cluster relationship. By
quantifying the functional overlap between clusters, our approach provides a
better ranking strategy of code solutions. Empirical results show that our
method achieves a remarkable results on pass@1 score. For instance, on the
Human-Eval benchmark, we achieve 69.66\% in pass@1 with Codex002, 75.31\% for
WizardCoder, 53.99\% for StarCoder and 60.55\% for CodeGen, which surpass the
state-of-the-arts solution ranking methods, such as CodeT and Coder-Reviewer on
the same CodeLLM with significant margin ($\approx 6.1\%$ improvement on
average). Comparing to the random sampling method, we can achieve an average
improvement of $\approx 23.07\%$ on Human-Eval and 17.64\% on MBPP. Even in
scenarios with limited test inputs, our approach demonstrates robustness and
superiority, marking a new state-of-the-arts in code generation reranking. | [
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] |
Convolutional neural networks (CNNs) models play a vital role in achieving
state-of-the-art performances in various technological fields. CNNs are not
limited to Natural Language Processing (NLP) or Computer Vision (CV) but also
have substantial applications in other technological domains, particularly in
cybersecurity. The reliability of CNN's models can be compromised because of
their susceptibility to adversarial attacks, which can be generated
effortlessly, easily applied, and transferred in real-world scenarios.
In this paper, we present a novel and comprehensive method to improve the
strength of attacks and assess the transferability of adversarial examples in
CNNs when such strength changes, as well as whether the transferability
property issue exists in computer network applications. In the context of our
study, we initially examined six distinct modes of attack: the Carlini and
Wagner (C&W), Fast Gradient Sign Method (FGSM), Iterative Fast Gradient Sign
Method (I-FGSM), Jacobian-based Saliency Map (JSMA), Limited-memory Broyden
fletcher Goldfarb Shanno (L-BFGS), and Projected Gradient Descent (PGD) attack.
We applied these attack techniques on two popular datasets: the CIC and UNSW
datasets. The outcomes of our experiment demonstrate that an improvement in
transferability occurs in the targeted scenarios for FGSM, JSMA, LBFGS, and
other attacks. Our findings further indicate that the threats to security posed
by adversarial examples, even in computer network applications, necessitate the
development of novel defense mechanisms to enhance the security of DL-based
techniques. | [
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] |
Inferring user preferences from the historical feedback of users is a
valuable problem in recommender systems. Conventional approaches often rely on
the assumption that user preferences in the feedback data are equivalent to the
real user preferences without additional noise, which simplifies the problem
modeling. However, there are various confounders during user-item interactions,
such as weather and even the recommendation system itself. Therefore,
neglecting the influence of confounders will result in inaccurate user
preferences and suboptimal performance of the model. Furthermore, the
unobservability of confounders poses a challenge in further addressing the
problem. To address these issues, we refine the problem and propose a more
rational solution. Specifically, we consider the influence of confounders,
disentangle them from user preferences in the latent space, and employ causal
graphs to model their interdependencies without specific labels. By cleverly
combining local and global causal graphs, we capture the user-specificity of
confounders on user preferences. We theoretically demonstrate the
identifiability of the obtained causal graph. Finally, we propose our model
based on Variational Autoencoders, named Causal Structure representation
learning of Confounders in latent space (CSC). We conducted extensive
experiments on one synthetic dataset and five real-world datasets,
demonstrating the superiority of our model. Furthermore, we demonstrate that
the learned causal representations of confounders are controllable, potentially
offering users fine-grained control over the objectives of their recommendation
lists with the learned causal graphs. | [
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] |
This work studies a central extremal graph theory problem inspired by a 1975
conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of
nodes) that maximize the number of edges without having 3- or 4-cycles. We
formulate this problem as a sequential decision-making problem and compare
AlphaZero, a neural network-guided tree search, with tabu search, a heuristic
local search method. Using either method, by introducing a curriculum --
jump-starting the search for larger graphs using good graphs found at smaller
sizes -- we improve the state-of-the-art lower bounds for several sizes. We
also propose a flexible graph-generation environment and a
permutation-invariant network architecture for learning to search in the space
of graphs. | [
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] |
The integration of Artificial Intelligence (AI) into education is a recent
development, with chatbots emerging as a noteworthy addition to this
transformative landscape. As online learning platforms rapidly advance,
students need to adapt swiftly to excel in this dynamic environment.
Consequently, understanding the acceptance of chatbots, particularly those
employing Large Language Model (LLM) such as Chat Generative Pretrained
Transformer (ChatGPT), Google Bard, and other interactive AI technologies, is
of paramount importance. However, existing research on chatbots in education
has overlooked key behavior-related aspects, such as Optimism, Innovativeness,
Discomfort, Insecurity, Transparency, Ethics, Interaction, Engagement, and
Accuracy, creating a significant literature gap. To address this gap, this
study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to
investigate the determinant of chatbots adoption in education among students,
considering the Technology Readiness Index (TRI) and Technology Acceptance
Model (TAM). Utilizing a five-point Likert scale for data collection, we
gathered a total of 185 responses, which were analyzed using R-Studio software.
We established 12 hypotheses to achieve its objectives. The results showed that
Optimism and Innovativeness are positively associated with Perceived Ease of
Use (PEOU) and Perceived Usefulness (PU). Conversely, Discomfort and Insecurity
negatively impact PEOU, with only Insecurity negatively affecting PU. These
findings provide insights for future technology designers, elucidating critical
user behavior factors influencing chatbots adoption and utilization in
educational contexts. | [
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] |
Differential privacy is a widely adopted framework designed to safeguard the
sensitive information of data providers within a data set. It is based on the
application of controlled noise at the interface between the server that stores
and processes the data, and the data consumers. Local differential privacy is a
variant that allows data providers to apply the privatization mechanism
themselves on their data individually. Therefore it provides protection also in
contexts in which the server, or even the data collector, cannot be trusted.
The introduction of noise, however, inevitably affects the utility of the data,
particularly by distorting the correlations between individual data components.
This distortion can prove detrimental to tasks such as causal discovery. In
this paper, we consider various well-known locally differentially private
mechanisms and compare the trade-off between the privacy they provide, and the
accuracy of the causal structure produced by algorithms for causal learning
when applied to data obfuscated by these mechanisms. Our analysis yields
valuable insights for selecting appropriate local differentially private
protocols for causal discovery tasks. We foresee that our findings will aid
researchers and practitioners in conducting locally private causal discovery. | [
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] |
The requirement of wireless data demands is increasingly high as the
sixth-generation (6G) technology evolves. Reconfigurable intelligent surface
(RIS) is promisingly deemed to be one of 6G techniques for extending service
coverage, reducing power consumption, and enhancing spectral efficiency. In
this article, we have provided some fundamentals of RIS deployment in theory
and hardware perspectives as well as utilization of artificial intelligence
(AI) and machine learning. We conducted an intelligent deployment of RIS
(i-Dris) prototype, including dual-band auto-guided vehicle (AGV) assisted RISs
associated with an mmWave base station (BS) and a receiver. The RISs are
deployed on the AGV with configured incident/reflection angles. While, both the
mmWave BS and receiver are associated with an edge server monitoring downlink
packets for obtaining system throughput. We have designed a federated
multi-agent reinforcement learning scheme associated with several AGV-RIS
agents and sub-agents per AGV-RIS consisting of the deployment of position,
height, orientation and elevation angles. The experimental results presented
the stationary measurement in different aspects and scenarios. The i-Dris can
reach up to 980 Mbps transmission throughput under a bandwidth of 100 MHz with
comparably low complexity as well as rapid deployment, which outperforms the
other existing works. At last, we highlight some opportunities and future
issues in leveraging RIS-empowered wireless communication networks. | [
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] |
In response to the burgeoning global demand for seafood and the challenges of
managing fish farms, we introduce an innovative IoT based environmental control
system that integrates sensor technology and advanced machine learning decision
support. Deploying a network of wireless sensors within the fish farm, we
continuously collect real-time data on crucial environmental parameters,
including water temperature, pH levels, humidity, and fish behavior. This data
undergoes meticulous preprocessing to ensure its reliability, including
imputation, outlier detection, feature engineering, and synchronization. At the
heart of our system are four distinct machine learning algorithms: Random
Forests predict and optimize water temperature and pH levels for the fish,
fostering their health and growth; Support Vector Machines (SVMs) function as
an early warning system, promptly detecting diseases and parasites in fish;
Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule
based on real-time environmental conditions, promoting resource efficiency and
fish productivity; Neural Networks manage the operation of critical equipment
like water pumps and heaters to maintain the desired environmental conditions
within the farm. These machine learning algorithms collaboratively make
real-time decisions to ensure that the fish farm's environmental conditions
align with predefined specifications, leading to improved fish health and
productivity while simultaneously reducing resource wastage, thereby
contributing to increased profitability and sustainability. This research
article showcases the power of data-driven decision support in fish farming,
promising to meet the growing demand for seafood while emphasizing
environmental responsibility and economic viability, thus revolutionizing the
future of fish farming. | [
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During the operation of industrial robots, unusual events may endanger the
safety of humans and the quality of production. When collecting data to detect
such cases, it is not ensured that data from all potentially occurring errors
is included as unforeseeable events may happen over time. Therefore, anomaly
detection (AD) delivers a practical solution, using only normal data to learn
to detect unusual events. We introduce a dataset that allows training and
benchmarking of anomaly detection methods for robotic applications based on
machine data which will be made publicly available to the research community.
As a typical robot task the dataset includes a pick-and-place application which
involves movement, actions of the end effector and interactions with the
objects of the environment. Since several of the contained anomalies are not
task-specific but general, evaluations on our dataset are transferable to other
robotics applications as well. Additionally, we present MVT-Flow (multivariate
time-series flow) as a new baseline method for anomaly detection: It relies on
deep-learning-based density estimation with normalizing flows, tailored to the
data domain by taking its structure into account for the architecture. Our
evaluation shows that MVT-Flow outperforms baselines from previous work by a
large margin of 6.2% in area under ROC. | [
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] |
Despite the promises of ML in education, its adoption in the classroom has
surfaced numerous issues regarding fairness, accountability, and transparency,
as well as concerns about data privacy and student consent. A root cause of
these issues is the lack of understanding of the complex dynamics of education,
including teacher-student interactions, collaborative learning, and classroom
environment. To overcome these challenges and fully utilize the potential of ML
in education, software practitioners need to work closely with educators and
students to fully understand the context of the data (the backbone of ML
applications) and collaboratively define the ML data specifications. To gain a
deeper understanding of such a collaborative process, we conduct ten co-design
sessions with ML software practitioners, educators, and students. In the
sessions, teachers and students work with ML engineers, UX designers, and legal
practitioners to define dataset characteristics for a given ML application. We
find that stakeholders contextualize data based on their domain and procedural
knowledge, proactively design data requirements to mitigate downstream harms
and data reliability concerns, and exhibit role-based collaborative strategies
and contribution patterns. Further, we find that beyond a seat at the table,
meaningful stakeholder participation in ML requires structured supports:
defined processes for continuous iteration and co-evaluation, shared contextual
data quality standards, and information scaffolds for both technical and
non-technical stakeholders to traverse expertise boundaries. | [
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] |
Large pre-trained sequence models, such as transformer-based architectures,
have been recently shown to have the capacity to carry out in-context learning
(ICL). In ICL, a decision on a new input is made via a direct mapping of the
input and of a few examples from the given task, serving as the task's context,
to the output variable. No explicit updates of model parameters are needed to
tailor the decision to a new task. Pre-training, which amounts to a form of
meta-learning, is based on the observation of examples from several related
tasks. Prior work has shown ICL capabilities for linear regression. In this
study, we leverage ICL to address the inverse problem of multiple-input and
multiple-output (MIMO) equalization based on a context given by pilot symbols.
A task is defined by the unknown fading channel and by the signal-to-noise
ratio (SNR) level, which may be known. To highlight the practical potential of
the approach, we allow for the presence of quantization of the received
signals. We demonstrate via numerical results that transformer-based ICL has a
threshold behavior, whereby, as the number of pre-training tasks grows, the
performance switches from that of a minimum mean squared error (MMSE) equalizer
with a prior determined by the pre-trained tasks to that of an MMSE equalizer
with the true data-generating prior. | [
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] |
Prediction of stock prices plays a significant role in aiding the
decision-making of investors. Considering its importance, a growing literature
has emerged trying to forecast stock prices with improved accuracy. In this
study, we introduce an innovative approach for forecasting stock prices with
greater accuracy. We incorporate external economic environment-related
information along with stock prices. In our novel approach, we improve the
performance of stock price prediction by taking into account variations due to
future expected macroeconomic policy changes as investors adjust their current
behavior ahead of time based on expected future macroeconomic policy changes.
Furthermore, we incorporate macroeconomic variables along with historical stock
prices to make predictions. Results from this strongly support the inclusion of
future economic policy changes along with current macroeconomic information. We
confirm the supremacy of our method over the conventional approach using
several tree-based machine-learning algorithms. Results are strongly conclusive
across various machine learning models. Our preferred model outperforms the
conventional approach with an RMSE value of 1.61 compared to an RMSE value of
1.75 from the conventional approach. | [
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] |
Accurate net load forecasting is vital for energy planning, aiding decisions
on trade and load distribution. However, assessing the performance of
forecasting models across diverse input variables, like temperature and
humidity, remains challenging, particularly for eliciting a high degree of
trust in the model outcomes. In this context, there is a growing need for
data-driven technological interventions to aid scientists in comprehending how
models react to both noisy and clean input variables, thus shedding light on
complex behaviors and fostering confidence in the outcomes. In this paper, we
present Forte, a visual analytics-based application to explore deep
probabilistic net load forecasting models across various input variables and
understand the error rates for different scenarios. With carefully designed
visual interventions, this web-based interface empowers scientists to derive
insights about model performance by simulating diverse scenarios, facilitating
an informed decision-making process. We discuss observations made using Forte
and demonstrate the effectiveness of visualization techniques to provide
valuable insights into the correlation between weather inputs and net load
forecasts, ultimately advancing grid capabilities by improving trust in
forecasting models. | [
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] |
Game solving is a similar, yet more difficult task than mastering a game.
Solving a game typically means to find the game-theoretic value (outcome given
optimal play), and optionally a full strategy to follow in order to achieve
that outcome. The AlphaZero algorithm has demonstrated super-human level play,
and its powerful policy and value predictions have also served as heuristics in
game solving. However, to solve a game and obtain a full strategy, a winning
response must be found for all possible moves by the losing player. This
includes very poor lines of play from the losing side, for which the AlphaZero
self-play process will not encounter. AlphaZero-based heuristics can be highly
inaccurate when evaluating these out-of-distribution positions, which occur
throughout the entire search. To address this issue, this paper investigates
applying online fine-tuning while searching and proposes two methods to learn
tailor-designed heuristics for game solving. Our experiments show that using
online fine-tuning can solve a series of challenging 7x7 Killall-Go problems,
using only 23.54% of computation time compared to the baseline without online
fine-tuning. Results suggest that the savings scale with problem size. Our
method can further be extended to any tree search algorithm for problem
solving. Our code is available at
https://rlg.iis.sinica.edu.tw/papers/neurips2023-online-fine-tuning-solver. | [
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This report examines whether advanced AIs that perform well in training will
be doing so in order to gain power later -- a behavior I call "scheming" (also
sometimes called "deceptive alignment"). I conclude that scheming is a
disturbingly plausible outcome of using baseline machine learning methods to
train goal-directed AIs sophisticated enough to scheme (my subjective
probability on such an outcome, given these conditions, is roughly 25%). In
particular: if performing well in training is a good strategy for gaining power
(as I think it might well be), then a very wide variety of goals would motivate
scheming -- and hence, good training performance. This makes it plausible that
training might either land on such a goal naturally and then reinforce it, or
actively push a model's motivations towards such a goal as an easy way of
improving performance. What's more, because schemers pretend to be aligned on
tests designed to reveal their motivations, it may be quite difficult to tell
whether this has occurred. However, I also think there are reasons for comfort.
In particular: scheming may not actually be such a good strategy for gaining
power; various selection pressures in training might work against schemer-like
goals (for example, relative to non-schemers, schemers need to engage in extra
instrumental reasoning, which might harm their training performance); and we
may be able to increase such pressures intentionally. The report discusses
these and a wide variety of other considerations in detail, and it suggests an
array of empirical research directions for probing the topic further. | [
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] |
Some stars are known to explode at the end of their lives, called supernovae
(SNe). The substantial amount of matter and energy that SNe release provides
significant feedback to star formation and gas dynamics in a galaxy. SNe
release a substantial amount of matter and energy to the interstellar medium,
resulting in significant feedback to star formation and gas dynamics in a
galaxy. While such feedback has a crucial role in galaxy formation and
evolution, in simulations of galaxy formation, it has only been implemented
using simple {\it sub-grid models} instead of numerically solving the evolution
of gas elements around SNe in detail due to a lack of resolution. We develop a
method combining machine learning and Gibbs sampling to predict how a supernova
(SN) affects the surrounding gas. The fidelity of our model in the thermal
energy and momentum distribution outperforms the low-resolution SN simulations.
Our method can replace the SN sub-grid models and help properly simulate
un-resolved SN feedback in galaxy formation simulations. We find that employing
our new approach reduces the necessary computational cost to $\sim$ 1 percent
compared to directly resolving SN feedback. | [
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] |
Existing work on jailbreak Multimodal Large Language Models (MLLMs) has
focused primarily on adversarial examples in model inputs, with less attention
to vulnerabilities in model APIs. To fill the research gap, we carry out the
following work: 1) We discover a system prompt leakage vulnerability in GPT-4V.
Through carefully designed dialogue, we successfully steal the internal system
prompts of GPT-4V. This finding indicates potential exploitable security risks
in MLLMs; 2)Based on the acquired system prompts, we propose a novel MLLM
jailbreaking attack method termed SASP (Self-Adversarial Attack via System
Prompt). By employing GPT-4 as a red teaming tool against itself, we aim to
search for potential jailbreak prompts leveraging stolen system prompts.
Furthermore, in pursuit of better performance, we also add human modification
based on GPT-4's analysis, which further improves the attack success rate to
98.7\%; 3) We evaluated the effect of modifying system prompts to defend
against jailbreaking attacks. Results show that appropriately designed system
prompts can significantly reduce jailbreak success rates. Overall, our work
provides new insights into enhancing MLLM security, demonstrating the important
role of system prompts in jailbreaking, which could be leveraged to greatly
facilitate jailbreak success rates while also holding the potential for
defending against jailbreaks. | [
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Accurately modeling protein 3D structure is essential for the design of
functional proteins. An important sub-task of structure modeling is protein
side-chain packing: predicting the conformation of side-chains (rotamers) given
the protein's backbone structure and amino-acid sequence. Conventional
approaches for this task rely on expensive sampling procedures over
hand-crafted energy functions and rotamer libraries. Recently, several deep
learning methods have been developed to tackle the problem in a data-driven
way, albeit with vastly different formulations (from image-to-image translation
to directly predicting atomic coordinates). Here, we frame the problem as a
joint regression over the side-chains' true degrees of freedom: the dihedral
$\chi$ angles. We carefully study possible objective functions for this task,
while accounting for the underlying symmetries of the task. We propose
Holographic Packer (H-Packer), a novel two-stage algorithm for side-chain
packing built on top of two light-weight rotationally equivariant neural
networks. We evaluate our method on CASP13 and CASP14 targets. H-Packer is
computationally efficient and shows favorable performance against conventional
physics-based algorithms and is competitive against alternative deep learning
solutions. | [
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] |
Diffusion models are at the vanguard of generative AI research with renowned
solutions such as ImageGen by Google Brain and DALL.E 3 by OpenAI.
Nevertheless, the potential merits of diffusion models for communication
engineering applications are not fully understood yet. In this paper, we aim to
unleash the power of generative AI for PHY design of constellation symbols in
communication systems. Although the geometry of constellations is predetermined
according to networking standards, e.g., quadrature amplitude modulation (QAM),
probabilistic shaping can design the probability of occurrence (generation) of
constellation symbols. This can help improve the information rate and decoding
performance of communication systems. We exploit the ``denoise-and-generate''
characteristics of denoising diffusion probabilistic models (DDPM) for
probabilistic constellation shaping. The key idea is to learn generating
constellation symbols out of noise, ``mimicking'' the way the receiver performs
symbol reconstruction. This way, we make the constellation symbols sent by the
transmitter, and what is inferred (reconstructed) at the receiver become as
similar as possible, resulting in as few mismatches as possible. Our results
show that the generative AI-based scheme outperforms deep neural network
(DNN)-based benchmark and uniform shaping, while providing network resilience
as well as robust out-of-distribution performance under low-SNR regimes and
non-Gaussian assumptions. Numerical evaluations highlight 30% improvement in
terms of cosine similarity and a threefold improvement in terms of mutual
information compared to DNN-based approach for 64-QAM geometry. | [
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] |
We introduce a new structure for memory neural networks, called feedforward
sequential memory networks (FSMN), which can learn long-term dependency without
using recurrent feedback. The proposed FSMN is a standard feedforward neural
networks equipped with learnable sequential memory blocks in the hidden layers.
In this work, we have applied FSMN to several language modeling (LM) tasks.
Experimental results have shown that the memory blocks in FSMN can learn
effective representations of long history. Experiments have shown that FSMN
based language models can significantly outperform not only feedforward neural
network (FNN) based LMs but also the popular recurrent neural network (RNN)
LMs. | [
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Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the
most powerful dynamic classifiers publicly known. The network itself and the
related learning algorithms are reasonably well documented to get an idea how
it works. This paper will shed more light into understanding how LSTM-RNNs
evolved and why they work impressively well, focusing on the early,
ground-breaking publications. We significantly improved documentation and fixed
a number of errors and inconsistencies that accumulated in previous
publications. To support understanding we as well revised and unified the
notation used. | [
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] |
Greedy Restrictive Boltzmann Machines yield an fairly low 0.72% error rate on
the famous MNIST database of handwritten digits. All that was required to
achieve this result was a high number of hidden layers consisting of many
neurons, and a graphics card to greatly speed up the rate of learning. | [
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] |
Background: An increasing volume of prostate biopsies and a world-wide
shortage of uro-pathologists puts a strain on pathology departments.
Additionally, the high intra- and inter-observer variability in grading can
result in over- and undertreatment of prostate cancer. Artificial intelligence
(AI) methods may alleviate these problems by assisting pathologists to reduce
workload and harmonize grading.
Methods: We digitized 6,682 needle biopsies from 976 participants in the
population based STHLM3 diagnostic study to train deep neural networks for
assessing prostate biopsies. The networks were evaluated by predicting the
presence, extent, and Gleason grade of malignant tissue for an independent test
set comprising 1,631 biopsies from 245 men. We additionally evaluated grading
performance on 87 biopsies individually graded by 23 experienced urological
pathologists from the International Society of Urological Pathology. We
assessed discriminatory performance by receiver operating characteristics (ROC)
and tumor extent predictions by correlating predicted millimeter cancer length
against measurements by the reporting pathologist. We quantified the
concordance between grades assigned by the AI and the expert urological
pathologists using Cohen's kappa.
Results: The performance of the AI to detect and grade cancer in prostate
needle biopsy samples was comparable to that of international experts in
prostate pathology. The AI achieved an area under the ROC curve of 0.997 for
distinguishing between benign and malignant biopsy cores, and 0.999 for
distinguishing between men with or without prostate cancer. The correlation
between millimeter cancer predicted by the AI and assigned by the reporting
pathologist was 0.96. For assigning Gleason grades, the AI achieved an average
pairwise kappa of 0.62. This was within the range of the corresponding values
for the expert pathologists (0.60 to 0.73). | [
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] |
Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO
recommends chest radiographs (CXRs) for TB screening, the limited availability
of CXR interpretation is a barrier. We trained a deep learning system (DLS) to
detect active pulmonary TB using CXRs from 9 countries across Africa, Asia, and
Europe, and utilized large-scale CXR pretraining, attention pooling, and noisy
student semi-supervised learning. Evaluation was on (1) a combined test set
spanning China, India, US, and Zambia, and (2) an independent mining population
in South Africa. Given WHO targets of 90% sensitivity and 70% specificity, the
DLS's operating point was prespecified to favor sensitivity over specificity.
On the combined test set, the DLS's ROC curve was above all 9 India-based
radiologists, with an AUC of 0.90 (95%CI 0.87-0.92). The DLS's sensitivity
(88%) was higher than the India-based radiologists (75% mean sensitivity),
p<0.001 for superiority; and its specificity (79%) was non-inferior to the
radiologists (84% mean specificity), p=0.004. Similar trends were observed
within HIV positive and sputum smear positive sub-groups, and in the South
Africa test set. We found that 5 US-based radiologists (where TB isn't endemic)
were more sensitive and less specific than the India-based radiologists (where
TB is endemic). The DLS also remained non-inferior to the US-based
radiologists. In simulations, using the DLS as a prioritization tool for
confirmatory testing reduced the cost per positive case detected by 40-80%
compared to using confirmatory testing alone. To conclude, our DLS generalized
to 5 countries, and merits prospective evaluation to assist cost-effective
screening efforts in radiologist-limited settings. Operating point flexibility
may permit customization of the DLS to account for site-specific factors such
as TB prevalence, demographics, clinical resources, and customary practice
patterns. | [
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] |
We establish an open framework for developing plan optimization models for
knowledge-based planning (KBP) in radiotherapy. Our framework includes
reference plans for 100 patients with head-and-neck cancer and high-quality
dose predictions from 19 KBP models that were developed by different research
groups during the OpenKBP Grand Challenge. The dose predictions were input to
four optimization models to form 76 unique KBP pipelines that generated 7600
plans. The predictions and plans were compared to the reference plans via: dose
score, which is the average mean absolute voxel-by-voxel difference in dose a
model achieved; the deviation in dose-volume histogram (DVH) criterion; and the
frequency of clinical planning criteria satisfaction. We also performed a
theoretical investigation to justify our dose mimicking models. The range in
rank order correlation of the dose score between predictions and their KBP
pipelines was 0.50 to 0.62, which indicates that the quality of the predictions
is generally positively correlated with the quality of the plans. Additionally,
compared to the input predictions, the KBP-generated plans performed
significantly better (P<0.05; one-sided Wilcoxon test) on 18 of 23 DVH
criteria. Similarly, each optimization model generated plans that satisfied a
higher percentage of criteria than the reference plans. Lastly, our theoretical
investigation demonstrated that the dose mimicking models generated plans that
are also optimal for a conventional planning model. This was the largest
international effort to date for evaluating the combination of KBP prediction
and optimization models. In the interest of reproducibility, our data and code
is freely available at https://github.com/ababier/open-kbp-opt. | [
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] |
We prove the following strong hardness result for learning: Given a
distribution of labeled examples from the hypercube such that there exists a
monomial consistent with $(1-\eps)$ of the examples, it is NP-hard to find a
halfspace that is correct on $(1/2+\eps)$ of the examples, for arbitrary
constants $\eps > 0$. In learning theory terms, weak agnostic learning of
monomials is hard, even if one is allowed to output a hypothesis from the much
bigger concept class of halfspaces. This hardness result subsumes a long line
of previous results, including two recent hardness results for the proper
learning of monomials and halfspaces. As an immediate corollary of our result
we show that weak agnostic learning of decision lists is NP-hard.
Our techniques are quite different from previous hardness proofs for
learning. We define distributions on positive and negative examples for
monomials whose first few moments match. We use the invariance principle to
argue that regular halfspaces (all of whose coefficients have small absolute
value relative to the total $\ell_2$ norm) cannot distinguish between
distributions whose first few moments match. For highly non-regular subspaces,
we use a structural lemma from recent work on fooling halfspaces to argue that
they are ``junta-like'' and one can zero out all but the top few coefficients
without affecting the performance of the halfspace. The top few coefficients
form the natural list decoding of a halfspace in the context of dictatorship
tests/Label Cover reductions.
We note that unlike previous invariance principle based proofs which are only
known to give Unique-Games hardness, we are able to reduce from a version of
Label Cover problem that is known to be NP-hard. This has inspired follow-up
work on bypassing the Unique Games conjecture in some optimal geometric
inapproximability results. | [
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] |
A computational challenge to validate the candidate disease genes identified
in a high-throughput genomic study is to elucidate the associations between the
set of candidate genes and disease phenotypes. The conventional gene set
enrichment analysis often fails to reveal associations between disease
phenotypes and the gene sets with a short list of poorly annotated genes,
because the existing annotations of disease causative genes are incomplete. We
propose a network-based computational approach called rcNet to discover the
associations between gene sets and disease phenotypes. Assuming coherent
associations between the genes ranked by their relevance to the query gene set,
and the disease phenotypes ranked by their relevance to the hidden target
disease phenotypes of the query gene set, we formulate a learning framework
maximizing the rank coherence with respect to the known disease phenotype-gene
associations. An efficient algorithm coupling ridge regression with label
propagation, and two variants are introduced to find the optimal solution of
the framework. We evaluated the rcNet algorithms and existing baseline methods
with both leave-one-out cross-validation and a task of predicting recently
discovered disease-gene associations in OMIM. The experiments demonstrated that
the rcNet algorithms achieved the best overall rankings compared to the
baselines. To further validate the reproducibility of the performance, we
applied the algorithms to identify the target diseases of novel candidate
disease genes obtained from recent studies of GWAS, DNA copy number variation
analysis, and gene expression profiling. The algorithms ranked the target
disease of the candidate genes at the top of the rank list in many cases across
all the three case studies. The rcNet algorithms are available as a webtool for
disease and gene set association analysis at
http://compbio.cs.umn.edu/dgsa_rcNet. | [
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] |
The Turing Test (TT) checks for human intelligence, rather than any putative
general intelligence. It involves repeated interaction requiring learning in
the form of adaption to the human conversation partner. It is a macro-level
post-hoc test in contrast to the definition of a Turing Machine (TM), which is
a prior micro-level definition. This raises the question of whether learning is
just another computational process, i.e. can be implemented as a TM. Here we
argue that learning or adaption is fundamentally different from computation,
though it does involve processes that can be seen as computations. To
illustrate this difference we compare (a) designing a TM and (b) learning a TM,
defining them for the purpose of the argument. We show that there is a
well-defined sequence of problems which are not effectively designable but are
learnable, in the form of the bounded halting problem. Some characteristics of
human intelligence are reviewed including it's: interactive nature, learning
abilities, imitative tendencies, linguistic ability and context-dependency. A
story that explains some of these is the Social Intelligence Hypothesis. If
this is broadly correct, this points to the necessity of a considerable period
of acculturation (social learning in context) if an artificial intelligence is
to pass the TT. Whilst it is always possible to 'compile' the results of
learning into a TM, this would not be a designed TM and would not be able to
continually adapt (pass future TTs). We conclude three things, namely that: a
purely "designed" TM will never pass the TT; that there is no such thing as a
general intelligence since it necessary involves learning; and that
learning/adaption and computation should be clearly distinguished. | [
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] |
Recently, much of the existing work in manifold learning has been done under
the assumption that the data is sampled from a manifold without boundaries and
singularities or that the functions of interest are evaluated away from such
points. At the same time, it can be argued that singularities and boundaries
are an important aspect of the geometry of realistic data.
In this paper we consider the behavior of graph Laplacians at points at or
near boundaries and two main types of other singularities: intersections, where
different manifolds come together and sharp "edges", where a manifold sharply
changes direction. We show that the behavior of graph Laplacian near these
singularities is quite different from that in the interior of the manifolds. In
fact, a phenomenon somewhat reminiscent of the Gibbs effect in the analysis of
Fourier series, can be observed in the behavior of graph Laplacian near such
points. Unlike in the interior of the domain, where graph Laplacian converges
to the Laplace-Beltrami operator, near singularities graph Laplacian tends to a
first-order differential operator, which exhibits different scaling behavior as
a function of the kernel width. One important implication is that while points
near the singularities occupy only a small part of the total volume, the
difference in scaling results in a disproportionately large contribution to the
total behavior. Another significant finding is that while the scaling behavior
of the operator is the same near different types of singularities, they are
very distinct at a more refined level of analysis.
We believe that a comprehensive understanding of these structures in addition
to the standard case of a smooth manifold can take us a long way toward better
methods for analysis of complex non-linear data and can lead to significant
progress in algorithm design. | [
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We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available. | [
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] |
Many real-world problems come with action spaces represented as feature
vectors. Although high-dimensional control is a largely unsolved problem, there
has recently been progress for modest dimensionalities. Here we report on a
successful attempt at addressing problems of dimensionality as high as $2000$,
of a particular form. Motivated by important applications such as
recommendation systems that do not fit the standard reinforcement learning
frameworks, we introduce Slate Markov Decision Processes (slate-MDPs). A
Slate-MDP is an MDP with a combinatorial action space consisting of slates
(tuples) of primitive actions of which one is executed in an underlying MDP.
The agent does not control the choice of this executed action and the action
might not even be from the slate, e.g., for recommendation systems for which
all recommendations can be ignored. We use deep Q-learning based on feature
representations of both the state and action to learn the value of whole
slates. Unlike existing methods, we optimize for both the combinatorial and
sequential aspects of our tasks. The new agent's superiority over agents that
either ignore the combinatorial or sequential long-term value aspect is
demonstrated on a range of environments with dynamics from a real-world
recommendation system. Further, we use deep deterministic policy gradients to
learn a policy that for each position of the slate, guides attention towards
the part of the action space in which the value is the highest and we only
evaluate actions in this area. The attention is used within a sequentially
greedy procedure leveraging submodularity. Finally, we show how introducing
risk-seeking can dramatically improve the agents performance and ability to
discover more far reaching strategies. | [
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] |
Robots assisting the disabled or elderly must perform complex manipulation
tasks and must adapt to the home environment and preferences of their user.
Learning from demonstration is a promising choice, that would allow the
non-technical user to teach the robot different tasks. However, collecting
demonstrations in the home environment of a disabled user is time consuming,
disruptive to the comfort of the user, and presents safety challenges. It would
be desirable to perform the demonstrations in a virtual environment. In this
paper we describe a solution to the challenging problem of behavior transfer
from virtual demonstration to a physical robot. The virtual demonstrations are
used to train a deep neural network based controller, which is using a Long
Short Term Memory (LSTM) recurrent neural network to generate trajectories. The
training process uses a Mixture Density Network (MDN) to calculate an error
signal suitable for the multimodal nature of demonstrations. The controller
learned in the virtual environment is transferred to a physical robot (a
Rethink Robotics Baxter). An off-the-shelf vision component is used to
substitute for geometric knowledge available in the simulation and an inverse
kinematics module is used to allow the Baxter to enact the trajectory. Our
experimental studies validate the three contributions of the paper: (1) the
controller learned from virtual demonstrations can be used to successfully
perform the manipulation tasks on a physical robot, (2) the LSTM+MDN
architectural choice outperforms other choices, such as the use of feedforward
networks and mean-squared error based training signals and (3) allowing
imperfect demonstrations in the training set also allows the controller to
learn how to correct its manipulation mistakes. | [
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0.038880445063114166,
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] |
Structural causal models (SCMs), also known as (nonparametric) structural
equation models (SEMs), are widely used for causal modeling purposes. In
particular, acyclic SCMs, also known as recursive SEMs, form a well-studied
subclass of SCMs that generalize causal Bayesian networks to allow for latent
confounders. In this paper, we investigate SCMs in a more general setting,
allowing for the presence of both latent confounders and cycles. We show that
in the presence of cycles, many of the convenient properties of acyclic SCMs do
not hold in general: they do not always have a solution; they do not always
induce unique observational, interventional and counterfactual distributions; a
marginalization does not always exist, and if it exists the marginal model does
not always respect the latent projection; they do not always satisfy a Markov
property; and their graphs are not always consistent with their causal
semantics. We prove that for SCMs in general each of these properties does hold
under certain solvability conditions. Our work generalizes results for SCMs
with cycles that were only known for certain special cases so far. We introduce
the class of simple SCMs that extends the class of acyclic SCMs to the cyclic
setting, while preserving many of the convenient properties of acyclic SCMs.
With this paper we aim to provide the foundations for a general theory of
statistical causal modeling with SCMs. | [
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] |
Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic
Hybrid Automata (SHA) are powerful formalisms to model and to reason about the
dynamics of biological systems, due to their ability to capture the
stochasticity inherent in biological processes. A classical question in formal
modelling with clear relevance to biological modelling is the model checking
problem. i.e. calculate the probability that a behaviour, expressed for
instance in terms of a certain temporal logic formula, may occur in a given
stochastic process. However, one may not only be interested in the notion of
satisfiability, but also in the capacity of a system to mantain a particular
emergent behaviour unaffected by the perturbations, caused e.g. from extrinsic
noise, or by possible small changes in the model parameters. To address this
issue, researchers from the verification community have recently proposed
several notions of robustness for temporal logic providing suitable definitions
of distance between a trajectory of a (deterministic) dynamical system and the
boundaries of the set of trajectories satisfying the property of interest. The
contributions of this paper are twofold. First, we extend the notion of
robustness to stochastic systems, showing that this naturally leads to a
distribution of robustness scores. By discussing two examples, we show how to
approximate the distribution of the robustness score and its key indicators:
the average robustness and the conditional average robustness. Secondly, we
show how to combine these indicators with the satisfaction probability to
address the system design problem, where the goal is to optimize some control
parameters of a stochastic model in order to best maximize robustness of the
desired specifications. | [
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] |
In this work we explore the use of reinforcement learning (RL) to help with
human decision making, combining state-of-the-art RL algorithms with an
application to prosthetics. Managing human-machine interaction is a problem of
considerable scope, and the simplification of human-robot interfaces is
especially important in the domains of biomedical technology and rehabilitation
medicine. For example, amputees who control artificial limbs are often required
to quickly switch between a number of control actions or modes of operation in
order to operate their devices. We suggest that by learning to anticipate
(predict) a user's behaviour, artificial limbs could take on an active role in
a human's control decisions so as to reduce the burden on their users.
Recently, we showed that RL in the form of general value functions (GVFs) could
be used to accurately detect a user's control intent prior to their explicit
control choices. In the present work, we explore the use of temporal-difference
learning and GVFs to predict when users will switch their control influence
between the different motor functions of a robot arm. Experiments were
performed using a multi-function robot arm that was controlled by muscle
signals from a user's body (similar to conventional artificial limb control).
Our approach was able to acquire and maintain forecasts about a user's
switching decisions in real time. It also provides an intuitive and reward-free
way for users to correct or reinforce the decisions made by the machine
learning system. We expect that when a system is certain enough about its
predictions, it can begin to take over switching decisions from the user to
streamline control and potentially decrease the time and effort needed to
complete tasks. This preliminary study therefore suggests a way to naturally
integrate human- and machine-based decision making systems. | [
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Non-linear dimensionality reduction techniques such as manifold learning
algorithms have become a common way for processing and analyzing
high-dimensional patterns that often have attached a target that corresponds to
the value of an unknown function. Their application to new points consists in
two steps: first, embedding the new data point into the low dimensional space
and then, estimating the function value on the test point from its neighbors in
the embedded space.
However, finding the low dimension representation of a test point, while easy
for simple but often not powerful enough procedures such as PCA, can be much
more complicated for methods that rely on some kind of eigenanalysis, such as
Spectral Clustering (SC) or Diffusion Maps (DM). Similarly, when a target
function is to be evaluated, averaging methods like nearest neighbors may give
unstable results if the function is noisy. Thus, the smoothing of the target
function with respect to the intrinsic, low-dimensional representation that
describes the geometric structure of the examined data is a challenging task.
In this paper we propose Auto-adaptive Laplacian Pyramids (ALP), an extension
of the standard Laplacian Pyramids model that incorporates a modified LOOCV
procedure that avoids the large cost of the standard one and offers the
following advantages: (i) it selects automatically the optimal function
resolution (stopping time) adapted to the data and its noise, (ii) it is easy
to apply as it does not require parameterization, (iii) it does not overfit the
training set and (iv) it adds no extra cost compared to other classical
interpolation methods. We illustrate numerically ALP's behavior on a synthetic
problem and apply it to the computation of the DM projection of new patterns
and to the extension to them of target function values on a radiation
forecasting problem over very high dimensional patterns. | [
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] |
High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript
quantification of gene isoforms. Since RNA-Seq data alone is often not
sufficient to accurately identify the read origins from the isoforms for
quantification, we propose to explore protein domain-domain interactions as
prior knowledge for integrative analysis with RNA-seq data. We introduce a
Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to
integrate protein domain-domain interaction network with short read alignments
for transcript abundance estimation. Based on our observation that the
abundances of the neighboring isoforms by domain-domain interactions in the
network are positively correlated, Net-RSTQ models the expression of the
neighboring transcripts as Dirichlet priors on the likelihood of the observed
read alignments against the transcripts in one gene. The transcript abundances
of all the genes are then jointly estimated with alternating optimization of
multiple EM problems. In simulation Net-RSTQ effectively improved isoform
transcript quantifications when isoform co-expressions correlate with their
interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an
ovarian cancer cell line, and a breast cancer cell line also showed that
Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In
the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the
transcript abundances estimated by Net-RSTQ are more informative for patient
sample classification of ovarian cancer, breast cancer and lung cancer. All
experimental results collectively support that Net-RSTQ is a promising approach
for isoform quantification. | [
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] |
In a wide range of statistical learning problems such as ranking, clustering
or metric learning among others, the risk is accurately estimated by
$U$-statistics of degree $d\geq 1$, i.e. functionals of the training data with
low variance that take the form of averages over $k$-tuples. From a
computational perspective, the calculation of such statistics is highly
expensive even for a moderate sample size $n$, as it requires averaging
$O(n^d)$ terms. This makes learning procedures relying on the optimization of
such data functionals hardly feasible in practice. It is the major goal of this
paper to show that, strikingly, such empirical risks can be replaced by
drastically computationally simpler Monte-Carlo estimates based on $O(n)$ terms
only, usually referred to as incomplete $U$-statistics, without damaging the
$O_{\mathbb{P}}(1/\sqrt{n})$ learning rate of Empirical Risk Minimization (ERM)
procedures. For this purpose, we establish uniform deviation results describing
the error made when approximating a $U$-process by its incomplete version under
appropriate complexity assumptions. Extensions to model selection, fast rate
situations and various sampling techniques are also considered, as well as an
application to stochastic gradient descent for ERM. Finally, numerical examples
are displayed in order to provide strong empirical evidence that the approach
we promote largely surpasses more naive subsampling techniques. | [
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] |
When using reinforcement learning (RL) algorithms to evaluate a policy it is
common, given a large state space, to introduce some form of approximation
architecture for the value function (VF). The exact form of this architecture
can have a significant effect on the accuracy of the VF estimate, however, and
determining a suitable approximation architecture can often be a highly complex
task. Consequently there is a large amount of interest in the potential for
allowing RL algorithms to adaptively generate approximation architectures.
We investigate a method of adapting approximation architectures which uses
feedback regarding the frequency with which an agent has visited certain states
to guide which areas of the state space to approximate with greater detail.
This method is "unsupervised" in the sense that it makes no direct reference to
reward or the VF estimate. We introduce an algorithm based upon this idea which
adapts a state aggregation approximation architecture on-line.
A common method of scoring a VF estimate is to weight the squared Bellman
error of each state-action by the probability of that state-action occurring.
Adopting this scoring method, and assuming $S$ states, we demonstrate
theoretically that - provided (1) the number of cells $X$ in the state
aggregation architecture is of order $\sqrt{S}\log_2{S}\ln{S}$ or greater, (2)
the policy and transition function are close to deterministic, and (3) the
prior for the transition function is uniformly distributed - our algorithm,
used in conjunction with a suitable RL algorithm, can guarantee a score which
is arbitrarily close to zero as $S$ becomes large. It is able to do this
despite having only $O(X \log_2S)$ space complexity and negligible time
complexity. The results take advantage of certain properties of the stationary
distributions of Markov chains. | [
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] |
We introduce the Deep Symbolic Network (DSN) model, which aims at becoming
the white-box version of Deep Neural Networks (DNN). The DSN model provides a
simple, universal yet powerful structure, similar to DNN, to represent any
knowledge of the world, which is transparent to humans. The conjecture behind
the DSN model is that any type of real world objects sharing enough common
features are mapped into human brains as a symbol. Those symbols are connected
by links, representing the composition, correlation, causality, or other
relationships between them, forming a deep, hierarchical symbolic network
structure. Powered by such a structure, the DSN model is expected to learn like
humans, because of its unique characteristics. First, it is universal, using
the same structure to store any knowledge. Second, it can learn symbols from
the world and construct the deep symbolic networks automatically, by utilizing
the fact that real world objects have been naturally separated by
singularities. Third, it is symbolic, with the capacity of performing causal
deduction and generalization. Fourth, the symbols and the links between them
are transparent to us, and thus we will know what it has learned or not - which
is the key for the security of an AI system. Fifth, its transparency enables it
to learn with relatively small data. Sixth, its knowledge can be accumulated.
Last but not least, it is more friendly to unsupervised learning than DNN. We
present the details of the model, the algorithm powering its automatic learning
ability, and describe its usefulness in different use cases. The purpose of
this paper is to generate broad interest to develop it within an open source
project centered on the Deep Symbolic Network (DSN) model towards the
development of general AI. | [
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] |
Modern communication networks have become very complicated and highly
dynamic, which makes them hard to model, predict and control. In this paper, we
develop a novel experience-driven approach that can learn to well control a
communication network from its own experience rather than an accurate
mathematical model, just as a human learns a new skill (such as driving,
swimming, etc). Specifically, we, for the first time, propose to leverage
emerging Deep Reinforcement Learning (DRL) for enabling model-free control in
communication networks; and present a novel and highly effective DRL-based
control framework, DRL-TE, for a fundamental networking problem: Traffic
Engineering (TE). The proposed framework maximizes a widely-used utility
function by jointly learning network environment and its dynamics, and making
decisions under the guidance of powerful Deep Neural Networks (DNNs). We
propose two new techniques, TE-aware exploration and actor-critic-based
prioritized experience replay, to optimize the general DRL framework
particularly for TE. To validate and evaluate the proposed framework, we
implemented it in ns-3, and tested it comprehensively with both representative
and randomly generated network topologies. Extensive packet-level simulation
results show that 1) compared to several widely-used baseline methods, DRL-TE
significantly reduces end-to-end delay and consistently improves the network
utility, while offering better or comparable throughput; 2) DRL-TE is robust to
network changes; and 3) DRL-TE consistently outperforms a state-ofthe-art DRL
method (for continuous control), Deep Deterministic Policy Gradient (DDPG),
which, however, does not offer satisfying performance. | [
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Dynamical systems are widely used in science and engineering to model systems
consisting of several interacting components. Often, they can be given a causal
interpretation in the sense that they not only model the evolution of the
states of the system's components over time, but also describe how their
evolution is affected by external interventions on the system that perturb the
dynamics. We introduce the formal framework of structural dynamical causal
models (SDCMs) that explicates the causal semantics of the system's components
as part of the model. SDCMs represent a dynamical system as a collection of
stochastic processes and specify the basic causal mechanisms that govern the
dynamics of each component as a structured system of random differential
equations of arbitrary order. SDCMs extend the versatile causal modeling
framework of structural causal models (SCMs), also known as structural equation
models (SEMs), by explicitly allowing for time-dependence. An SDCM can be
thought of as the stochastic-process version of an SCM, where the static random
variables of the SCM are replaced by dynamic stochastic processes and their
derivatives. We provide the foundations for a theory of SDCMs, by (i) formally
defining SDCMs, their solutions, stochastic interventions, and a graphical
representation; (ii) studying existence and uniqueness of the solutions for
given initial conditions; (iii) providing Markov properties for SDCMs with
initial conditions; (iv) discussing under which conditions SDCMs equilibrate to
SCMs as time tends to infinity; (v) relating the properties of the SDCM to
those of the equilibrium SCM. This correspondence enables one to leverage the
wealth of statistical tools and discovery methods available for SCMs when
studying the causal semantics of a large class of stochastic dynamical systems.
The theory is illustrated with examples from different scientific domains. | [
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] |
Efficient label acquisition processes are key to obtaining robust
classifiers. However, data labeling is often challenging and subject to high
levels of label noise. This can arise even when classification targets are well
defined, if instances to be labeled are more difficult than the prototypes used
to define the class, leading to disagreements among the expert community. Here,
we enable efficient training of deep neural networks. From low-confidence
labels, we iteratively improve their quality by simultaneous learning of
machines and experts. We call it Human And Machine co-LEarning Technique
(HAMLET). Throughout the process, experts become more consistent, while the
algorithm provides them with explainable feedback for confirmation. HAMLET uses
a neural embedding function and a memory module filled with diverse reference
embeddings from different classes. Its output includes classification labels
and highly relevant reference embeddings as explanation. We took the study of
brain monitoring at intensive care unit (ICU) as an application of HAMLET on
continuous electroencephalography (cEEG) data. Although cEEG monitoring yields
large volumes of data, labeling costs and difficulty make it hard to build a
classifier. Additionally, while experts agree on the labels of clear-cut
examples of cEEG patterns, labeling many real-world cEEG data can be extremely
challenging. Thus, a large minority of sequences might be mislabeled. HAMLET
has shown significant performance gain against deep learning and other
baselines, increasing accuracy from 7.03% to 68.75% on challenging inputs.
Besides improved performance, clinical experts confirmed the interpretability
of those reference embeddings in helping explaining the classification results
by HAMLET. | [
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] |
Several recent studies have demonstrated the promise of deep visuomotor
policies for robot manipulator control. Despite impressive progress, these
systems are known to be vulnerable to physical disturbances, such as accidental
or adversarial bumps that make them drop the manipulated object. They also tend
to be distracted by visual disturbances such as objects moving in the robot's
field of view, even if the disturbance does not physically prevent the
execution of the task. In this paper, we propose an approach for augmenting a
deep visuomotor policy trained through demonstrations with Task Focused visual
Attention (TFA). The manipulation task is specified with a natural language
text such as `move the red bowl to the left'. This allows the visual attention
component to concentrate on the current object that the robot needs to
manipulate. We show that even in benign environments, the TFA allows the policy
to consistently outperform a variant with no attention mechanism. More
importantly, the new policy is significantly more robust: it regularly recovers
from severe physical disturbances (such as bumps causing it to drop the object)
from which the baseline policy, i.e. with no visual attention, almost never
recovers. In addition, we show that the proposed policy performs correctly in
the presence of a wide class of visual disturbances, exhibiting a behavior
reminiscent of human selective visual attention experiments. Our proposed
approach consists of a VAE-GAN network which encodes the visual input and feeds
it to a Motor network that moves the robot joints. Also, our approach benefits
from a teacher network for the TFA that leverages textual input command to
robustify the visual encoder against various types of disturbances. | [
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0.0283494982868433,
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0.014776507392525673,
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] |
This book is a graduate-level introduction to probabilistic programming. It
not only provides a thorough background for anyone wishing to use a
probabilistic programming system, but also introduces the techniques needed to
design and build these systems. It is aimed at people who have an
undergraduate-level understanding of either or, ideally, both probabilistic
machine learning and programming languages.
We start with a discussion of model-based reasoning and explain why
conditioning is a foundational computation central to the fields of
probabilistic machine learning and artificial intelligence. We then introduce a
first-order probabilistic programming language (PPL) whose programs correspond
to graphical models with a known, finite, set of random variables. In the
context of this PPL we introduce fundamental inference algorithms and describe
how they can be implemented.
We then turn to higher-order probabilistic programming languages. Programs in
such languages can define models with dynamic computation graphs, which may not
instantiate the same set of random variables in each execution. Inference
requires methods that generate samples by repeatedly evaluating the program.
Foundational algorithms for this kind of language are discussed in the context
of an interface between program executions and an inference controller.
Finally we consider the intersection of probabilistic and differentiable
programming. We begin with a discussion of automatic differentiation, and how
it can be used to implement efficient inference methods based on Hamiltonian
Monte Carlo. We then discuss gradient-based maximum likelihood estimation in
programs that are parameterized using neural networks, how to amortize
inference using by learning neural approximations to the program posterior, and
how language features impact the design of deep probabilistic programming
systems. | [
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] |
The young infant explores its body, its sensorimotor system, and the
immediately accessible parts of its environment, over the course of a few
months creating a model of peripersonal space useful for reaching and grasping
objects around it. Drawing on constraints from the empirical literature on
infant behavior, we present a preliminary computational model of this learning
process, implemented and evaluated on a physical robot. The learning agent
explores the relationship between the configuration space of the arm, sensing
joint angles through proprioception, and its visual perceptions of the hand and
grippers. The resulting knowledge is represented as the peripersonal space
(PPS) graph, where nodes represent states of the arm, edges represent safe
movements, and paths represent safe trajectories from one pose to another. In
our model, the learning process is driven by intrinsic motivation. When
repeatedly performing an action, the agent learns the typical result, but also
detects unusual outcomes, and is motivated to learn how to make those unusual
results reliable. Arm motions typically leave the static background unchanged,
but occasionally bump an object, changing its static position. The reach action
is learned as a reliable way to bump and move an object in the environment.
Similarly, once a reliable reach action is learned, it typically makes a
quasi-static change in the environment, moving an object from one static
position to another. The unusual outcome is that the object is accidentally
grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically
with the hand. Learning to make grasps reliable is more complex than for
reaches, but we demonstrate significant progress. Our current results are steps
toward autonomous sensorimotor learning of motion, reaching, and grasping in
peripersonal space, based on unguided exploration and intrinsic motivation. | [
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] |
TensorFlow is a popular deep learning framework used by data scientists to
solve a wide-range of machine learning and deep learning problems such as image
classification and speech recognition. It also operates at a large scale and in
heterogeneous environments --- it allows users to train neural network models
or deploy them for inference using GPUs, CPUs and deep learning specific
custom-designed hardware such as TPUs. Even though TensorFlow supports a
variety of optimized backends, realizing the best performance using a backend
may require additional efforts. For instance, getting the best performance from
a CPU backend requires careful tuning of its threading model. Unfortunately,
the best tuning approach used today is manual, tedious, time-consuming, and,
more importantly, may not guarantee the best performance.
In this paper, we develop an automatic approach, called TensorTuner, to
search for optimal parameter settings of TensorFlow's threading model for CPU
backends. We evaluate TensorTuner on both Eigen and Intel's MKL CPU backends
using a set of neural networks from TensorFlow's benchmarking suite. Our
evaluation results demonstrate that the parameter settings found by TensorTuner
produce 2% to 123% performance improvement for the Eigen CPU backend and 1.5%
to 28% performance improvement for the MKL CPU backend over the performance
obtained using their best-known parameter settings. This highlights the fact
that the default parameter settings in Eigen CPU backend are not the ideal
settings; and even for a carefully hand-tuned MKL backend, the settings may be
sub-optimal. Our evaluations also revealed that TensorTuner is efficient at
finding the optimal settings --- it is able to converge to the optimal settings
quickly by pruning more than 90% of the parameter search space. | [
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Optimal stopping is the problem of deciding when to stop a stochastic system
to obtain the greatest reward, arising in numerous application areas such as
finance, healthcare and marketing. State-of-the-art methods for
high-dimensional optimal stopping involve approximating the value function or
the continuation value, and then using that approximation within a greedy
policy. Although such policies can perform very well, they are generally not
guaranteed to be interpretable; that is, a decision maker may not be able to
easily see the link between the current system state and the policy's action.
In this paper, we propose a new approach to optimal stopping, wherein the
policy is represented as a binary tree, in the spirit of naturally
interpretable tree models commonly used in machine learning. We show that the
class of tree policies is rich enough to approximate the optimal policy. We
formulate the problem of learning such policies from observed trajectories of
the stochastic system as a sample average approximation (SAA) problem. We prove
that the SAA problem converges under mild conditions as the sample size
increases, but that computationally even immediate simplifications of the SAA
problem are theoretically intractable. We thus propose a tractable heuristic
for approximately solving the SAA problem, by greedily constructing the tree
from the top down. We demonstrate the value of our approach by applying it to
the canonical problem of option pricing, using both synthetic instances and
instances using real S&P-500 data. Our method obtains policies that (1)
outperform state-of-the-art non-interpretable methods, based on
simulation-regression and martingale duality, and (2) possess a remarkably
simple and intuitive structure. | [
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The Unbiased Learning-to-Rank framework has been recently proposed as a
general approach to systematically remove biases, such as position bias, from
learning-to-rank models. The method takes two steps - estimating click
propensities and using them to train unbiased models. Most common methods
proposed in the literature for estimating propensities involve some degree of
intervention in the live search engine. An alternative approach proposed
recently uses an Expectation Maximization (EM) algorithm to estimate
propensities by using ranking features for estimating relevances. In this work
we propose a novel method to directly estimate propensities which does not use
any intervention in live search or rely on modeling relevance. Rather, we take
advantage of the fact that the same query-document pair may naturally change
ranks over time. This typically occurs for eCommerce search because of change
of popularity of items over time, existence of time dependent ranking features,
or addition or removal of items to the index (an item getting sold or a new
item being listed). However, our method is general and can be applied to any
search engine for which the rank of the same document may naturally change over
time for the same query. We derive a simple likelihood function that depends on
propensities only, and by maximizing the likelihood we are able to get
estimates of the propensities. We apply this method to eBay search data to
estimate click propensities for web and mobile search and compare these with
estimates using the EM method. We also use simulated data to show that the
method gives reliable estimates of the "true" simulated propensities. Finally,
we train an unbiased learning-to-rank model for eBay search using the estimated
propensities and show that it outperforms both baselines - one without position
bias correction and one with position bias correction using the EM method. | [
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] |
We advocate an optimization-centric view on and introduce a novel
generalization of Bayesian inference. Our inspiration is the representation of
Bayes' rule as infinite-dimensional optimization problem (Csiszar, 1975;
Donsker and Varadhan; 1975, Zellner; 1988). First, we use it to prove an
optimality result of standard Variational Inference (VI): Under the proposed
view, the standard Evidence Lower Bound (ELBO) maximizing VI posterior is
preferable to alternative approximations of the Bayesian posterior. Next, we
argue for generalizing standard Bayesian inference. The need for this arises in
situations of severe misalignment between reality and three assumptions
underlying standard Bayesian inference: (1) Well-specified priors, (2)
well-specified likelihoods, (3) the availability of infinite computing power.
Our generalization addresses these shortcomings with three arguments and is
called the Rule of Three (RoT). We derive it axiomatically and recover existing
posteriors as special cases, including the Bayesian posterior and its
approximation by standard VI. In contrast, approximations based on alternative
ELBO-like objectives violate the axioms. Finally, we study a special case of
the RoT that we call Generalized Variational Inference (GVI). GVI posteriors
are a large and tractable family of belief distributions specified by three
arguments: A loss, a divergence and a variational family. GVI posteriors have
appealing properties, including consistency and an interpretation as
approximate ELBO. The last part of the paper explores some attractive
applications of GVI in popular machine learning models, including robustness
and more appropriate marginals. After deriving black box inference schemes for
GVI posteriors, their predictive performance is investigated on Bayesian Neural
Networks and Deep Gaussian Processes, where GVI can comprehensively improve
upon existing methods. | [
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In e-commerce, a user tends to search for the desired product by issuing a
query to the search engine and examining the retrieved results. If the search
engine was successful in correctly understanding the user's query, it will
return results that correspond to the products whose attributes match the terms
in the query that are representative of the query's product intent. However,
the search engine may fail to retrieve results that satisfy the query's product
intent and thus degrading user experience due to different issues in query
processing: (i) when multiple terms are present in a query it may fail to
determine the relevant terms that are representative of the query's product
intent, and (ii) it may suffer from vocabulary gap between the terms in the
query and the product's description, i.e., terms used in the query are
semantically similar but different from the terms in the product description.
Hence, identifying the terms that describe the query's product intent and
predicting additional terms that describe the query's product intent better
than the existing query terms to the search engine is an essential task in
e-commerce search. In this paper, we leverage the historical query
reformulation logs of a major e-commerce retailer to develop distant-supervised
approaches to solve both these problems. Our approaches exploit the fact that
the significance of a term is dependent upon the context (other terms in the
neighborhood) in which it is used in order to learn the importance of the term
towards the query's product intent. We show that identifying and emphasizing
the terms that define the query's product intent leads to a 3% improvement in
ranking. Moreover, for the tasks of identifying the important terms in a query
and for predicting the additional terms that represent product intent,
experiments illustrate that our approaches outperform the non-contextual
baselines. | [
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] |
Intelligent Tutoring Systems (ITSs) have been developed to provide students
with personalized learning experiences by adaptively generating learning paths
optimized for each individual. Within the vast scope of ITS, score prediction
stands out as an area of study that enables students to construct individually
realistic goals based on their current position. Via the expected score
provided by the ITS, a student can instantaneously compare one's expected score
to one's actual score, which directly corresponds to the reliability that the
ITS can instill. In other words, refining the precision of predicted scores
strictly correlates to the level of confidence that a student may have with an
ITS, which will evidently ensue improved student engagement. However, previous
studies have solely concentrated on improving the performance of a prediction
model, largely lacking focus on the benefits generated by its practical
application. In this paper, we demonstrate that the accuracy of the score
prediction model deployed in a real-world setting significantly impacts user
engagement by providing empirical evidence. To that end, we apply a
state-of-the-art deep attentive neural network-based score prediction model to
Santa, a multi-platform English ITS with approximately 780K users in South
Korea that exclusively focuses on the TOEIC (Test of English for International
Communications) standardized examinations. We run a controlled A/B test on the
ITS with two models, respectively based on collaborative filtering and deep
attentive neural networks, to verify whether the more accurate model engenders
any student engagement. The results conclude that the attentive model not only
induces high student morale (e.g. higher diagnostic test completion ratio,
number of questions answered, etc.) but also encourages active engagement (e.g.
higher purchase rate, improved total profit, etc.) on Santa. | [
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] |
Existing Collaborative Filtering (CF) methods are mostly designed based on
the idea of matching, i.e., by learning user and item embeddings from data
using shallow or deep models, they try to capture the associative relevance
patterns in data, so that a user embedding can be matched with relevant item
embeddings using designed or learned similarity functions. However, as a
cognition rather than a perception intelligent task, recommendation requires
not only the ability of pattern recognition and matching from data, but also
the ability of cognitive reasoning in data. In this paper, we propose to
advance Collaborative Filtering (CF) to Collaborative Reasoning (CR), which
means that each user knows part of the reasoning space, and they collaborate
for reasoning in the space to estimate preferences for each other. Technically,
we propose a Neural Collaborative Reasoning (NCR) framework to bridge learning
and reasoning. Specifically, we integrate the power of representation learning
and logical reasoning, where representations capture similarity patterns in
data from perceptual perspectives, and logic facilitates cognitive reasoning
for informed decision making. An important challenge, however, is to bridge
differentiable neural networks and symbolic reasoning in a shared architecture
for optimization and inference. To solve the problem, we propose a modularized
reasoning architecture, which learns logical operations such as AND ($\wedge$),
OR ($\vee$) and NOT ($\neg$) as neural modules for implication reasoning
($\rightarrow$). In this way, logical expressions can be equivalently organized
as neural networks, so that logical reasoning and prediction can be conducted
in a continuous space. Experiments on real-world datasets verified the
advantages of our framework compared with both shallow, deep and reasoning
models. | [
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Deep Interest Network (DIN) is a state-of-the-art model which uses attention
mechanism to capture user interests from historical behaviors. User interests
intuitively follow a hierarchical pattern such that users generally show
interests from a higher-level then to a lower-level abstraction. Modeling such
an interest hierarchy in an attention network can fundamentally improve the
representation of user behaviors. We, therefore, propose an improvement over
DIN to model arbitrary interest hierarchy: Deep Interest with Hierarchical
Attention Network (DHAN). In this model, a multi-dimensional hierarchical
structure is introduced on the first attention layer which attends to an
individual item, and the subsequent attention layers in the same dimension
attend to higher-level hierarchy built on top of the lower corresponding
layers. To enable modeling of multiple dimensional hierarchies, an expanding
mechanism is introduced to capture one to many hierarchies. This design enables
DHAN to attend different importance to different hierarchical abstractions thus
can fully capture user interests at different dimensions (e.g. category, price,
or brand).To validate our model, a simplified DHAN has applied to Click-Through
Rate (CTR) prediction and our experimental results on three public datasets
with two levels of the one-dimensional hierarchy only by category. It shows the
superiority of DHAN with significant AUC uplift from 12% to 21% over DIN. DHAN
is also compared with another state-of-the-art model Deep Interest Evolution
Network (DIEN), which models temporal interest. The simplified DHAN also gets
slight AUC uplift from 1.0% to 1.7% over DIEN. A potential future work can be a
combination of DHAN and DIEN to model both temporal and hierarchical interests. | [
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] |
In online advertising, the Internet users may be exposed to a sequence of
different ad campaigns, i.e., display ads, search, or referrals from multiple
channels, before led up to any final sales conversion and transaction. For both
campaigners and publishers, it is fundamentally critical to estimate the
contribution from ad campaign touch-points during the customer journey
(conversion funnel) and assign the right credit to the right ad exposure
accordingly. However, the existing research on the multi-touch attribution
problem lacks a principled way of utilizing the users' pre-conversion actions
(i.e., clicks), and quite often fails to model the sequential patterns among
the touch points from a user's behavior data. To make it worse, the current
industry practice is merely employing a set of arbitrary rules as the
attribution model, e.g., the popular last-touch model assigns 100% credit to
the final touch-point regardless of actual attributions. In this paper, we
propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch
attribution problem. It learns the attribution values through an attention
mechanism directly from the conversion estimation objective. To achieve this,
we utilize sequence-to-sequence prediction for user clicks, and combine both
post-view and post-click attribution patterns together for the final conversion
estimation. To quantitatively benchmark attribution models, we also propose a
novel yet practical attribution evaluation scheme through the proxy of budget
allocation (under the estimated attributions) over ad channels. The
experimental results on two real datasets demonstrate the significant
performance gains of our attribution model against the state of the art. | [
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] |
The history of learning for control has been an exciting back and forth
between two broad classes of algorithms: planning and reinforcement learning.
Planning algorithms effectively reason over long horizons, but assume access to
a local policy and distance metric over collision-free paths. Reinforcement
learning excels at learning policies and the relative values of states, but
fails to plan over long horizons. Despite the successes of each method in
various domains, tasks that require reasoning over long horizons with limited
feedback and high-dimensional observations remain exceedingly challenging for
both planning and reinforcement learning algorithms. Frustratingly, these sorts
of tasks are potentially the most useful, as they are simple to design (a human
only need to provide an example goal state) and avoid reward shaping, which can
bias the agent towards finding a sub-optimal solution. We introduce a general
control algorithm that combines the strengths of planning and reinforcement
learning to effectively solve these tasks. Our aim is to decompose the task of
reaching a distant goal state into a sequence of easier tasks, each of which
corresponds to reaching a subgoal. Planning algorithms can automatically find
these waypoints, but only if provided with suitable abstractions of the
environment -- namely, a graph consisting of nodes and edges. Our main insight
is that this graph can be constructed via reinforcement learning, where a
goal-conditioned value function provides edge weights, and nodes are taken to
be previously seen observations in a replay buffer. Using graph search over our
replay buffer, we can automatically generate this sequence of subgoals, even in
image-based environments. Our algorithm, search on the replay buffer (SoRB),
enables agents to solve sparse reward tasks over one hundred steps, and
generalizes substantially better than standard RL algorithms. | [
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Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven
webs of causality and policy variables. FCMs are fuzzy signed directed graphs
that allow degrees of causal influence and event occurrence. Such causal models
can simulate a wide range of policy scenarios and decision processes. Their
directed loops or cycles directly model causal feedback. Their nonlinear
dynamics permit forward-chaining inference from input causes and policy options
to output effects. Users can add detailed dynamics and feedback links directly
to the causal model or infer them with statistical learning laws. Users can
fuse or combine FCMs from multiple experts by weighting and adding the
underlying fuzzy edge matrices and do so recursively if needed. The combined
FCM tends to better represent domain knowledge as the expert sample size
increases if the expert sample approximates a random sample. Many causal models
use more restrictive directed acyclic graphs (DAGs) and Bayesian probabilities.
DAGs do not model causal feedback because they do not contain closed loops.
Combining DAGs also tends to produce cycles and thus tends not to produce a new
DAG. Combining DAGs tends to produce a FCM. FCM causal influence is also
transitive whereas probabilistic causal influence is not transitive in general.
Overall: FCMs trade the numerical precision of probabilistic DAGs for pattern
prediction, faster and scalable computation, ease of combination, and richer
feedback representation. We show how FCMs can apply to problems of public
support for insurgency and terrorism and to US-China conflict relations in
Graham Allison's Thucydides-trap framework. The appendix gives the textual
justification of the Thucydides-trap FCM. It also extends our earlier theorem
[Osoba-Kosko2017] to a more general result that shows the transitive and total
causal influence that upstream concept nodes exert on downstream nodes. | [
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] |
Bipartite graphs have been used to represent data relationships in many
data-mining applications such as in E-commerce recommendation systems. Since
learning in graph space is more complicated than in Euclidian space, recent
studies have extensively utilized neural nets to effectively and efficiently
embed a graph's nodes into a multidimensional space. However, this embedding
method has not yet been applied to large-scale bipartite graphs. Existing
techniques either cannot be scaled to large-scale bipartite graphs that have
limited labels or cannot exploit the unique structure of bipartite graphs,
which have distinct node features in two domains. Thus, we propose Cascade
Bipartite Graph Neural Networks, Cascade-BGNN, a novel node representation
learning for bipartite graphs that is domain-consistent, self-supervised, and
efficient. To efficiently aggregate information both across and within the two
partitions of a bipartite graph, BGNN utilizes a customized Inter-domain
Message Passing (IDMP) and Intra-domain Alignment (IDA), which is our
adaptation of adversarial learning, for message aggregation across and within
partitions, respectively. BGNN is trained in a self-supervised manner.
Moreover, we formulate a multi-layer BGNN in a cascaded training manner to
enable multi-hop relationship modeling while improving training efficiency.
Extensive experiments on several datasets of varying scales verify the
effectiveness and efficiency of BGNN over baselines. Our design is further
affirmed through theoretical analysis for domain alignment. The scalability of
BGNN is additionally verified through its demonstrated rapid training speed and
low memory cost over a large-scale real-world bipartite graph. | [
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] |
In team-based invasion sports such as soccer and basketball, analytics is
important for teams to understand their performance and for audiences to
understand matches better. The present work focuses on performing visual
analytics to evaluate the value of any kind of event occurring in a sports
match with a continuous parameter space. Here, the continuous parameter space
involves the time, location, score, and other parameters. Because the
spatiotemporal data used in such analytics is a low-level representation and
has a very large size, however, traditional analytics may need to discretize
the continuous parameter space (e.g., subdivide the playing area) or use a
local feature to limit the analysis to specific events (e.g., only shots).
These approaches make evaluation impossible for any kind of event with a
continuous parameter space. To solve this problem, we consider a whole match as
a Markov chain of significant events, so that event values can be estimated
with a continuous parameter space by solving the Markov chain with a machine
learning model. The significant events are first extracted by considering the
time-varying distribution of players to represent the whole match. Then, the
extracted events are redefined as different states with the continuous
parameter space and built as a Markov chain so that a Markov reward process can
be applied. Finally, the Markov reward process is solved by a customized
fitted-value iteration algorithm so that the event values with the continuous
parameter space can be predicted by a regression model. As a result, the event
values can be visually inspected over the whole playing field under arbitrary
given conditions. Experimental results with real soccer data show the
effectiveness of the proposed system. | [
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Parking, matching, scheduling, and routing are common problems in train
maintenance. In particular, train units are commonly maintained and cleaned at
dedicated shunting yards. The planning problem that results from such
situations is referred to as the Train Unit Shunting Problem (TUSP). This
problem involves matching arriving train units to service tasks and determining
the schedule for departing trains. The TUSP is an important problem as it is
used to determine the capacity of shunting yards and arises as a sub-problem of
more general scheduling and planning problems. In this paper, we consider the
case of the Dutch Railways (NS) TUSP. As the TUSP is complex, NS currently uses
a local search (LS) heuristic to determine if an instance of the TUSP has a
feasible solution. Given the number of shunting yards and the size of the
planning problems, improving the evaluation speed of the LS brings significant
computational gain. In this work, we use a machine learning approach that
complements the LS and accelerates the search process. We use a Deep Graph
Convolutional Neural Network (DGCNN) model to predict the feasibility of
solutions obtained during the run of the LS heuristic. We use this model to
decide whether to continue or abort the search process. In this way, the
computation time is used more efficiently as it is spent on instances that are
more likely to be feasible. Using simulations based on real-life instances of
the TUSP, we show how our approach improves upon the previous method on
prediction accuracy and leads to computational gains for the decision-making
process. | [
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] |
Experimental design is a process of obtaining a product with target property
via experimentation. Bayesian optimization offers a sample-efficient tool for
experimental design when experiments are expensive. Often, expert experimenters
have 'hunches' about the behavior of the experimental system, offering
potentials to further improve the efficiency. In this paper, we consider
per-variable monotonic trend in the underlying property that results in a
unimodal trend in those variables for a target value optimization. For example,
sweetness of a candy is monotonic to the sugar content. However, to obtain a
target sweetness, the utility of the sugar content becomes a unimodal function,
which peaks at the value giving the target sweetness and falls off both ways.
In this paper, we propose a novel method to solve such problems that achieves
two main objectives: a) the monotonicity information is used to the fullest
extent possible, whilst ensuring that b) the convergence guarantee remains
intact. This is achieved by a two-stage Gaussian process modeling, where the
first stage uses the monotonicity trend to model the underlying property, and
the second stage uses `virtual' samples, sampled from the first, to model the
target value optimization function. The process is made theoretically
consistent by adding appropriate adjustment factor in the posterior
computation, necessitated because of using the `virtual' samples. The proposed
method is evaluated through both simulations and real world experimental design
problems of a) new short polymer fiber with the target length, and b) designing
of a new three dimensional porous scaffolding with a target porosity. In all
scenarios our method demonstrates faster convergence than the basic Bayesian
optimization approach not using such `hunches'. | [
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] |
This work presents an overarching perspective on the role that machine
intelligence can play in enhancing human abilities, especially those that have
been diminished due to injury or illness. As a primary contribution, we develop
the hypothesis that assistive devices, and specifically artificial arms and
hands, can and should be viewed as agents in order for us to most effectively
improve their collaboration with their human users. We believe that increased
agency will enable more powerful interactions between human users and next
generation prosthetic devices, especially when the sensorimotor space of the
prosthetic technology greatly exceeds the conventional control and
communication channels available to a prosthetic user. To more concretely
examine an agency-based view on prosthetic devices, we propose a new schema for
interpreting the capacity of a human-machine collaboration as a function of
both the human's and machine's degrees of agency. We then introduce the idea of
communicative capital as a way of thinking about the communication resources
developed by a human and a machine during their ongoing interaction. Using this
schema of agency and capacity, we examine the benefits and disadvantages of
increasing the agency of a prosthetic limb. To do so, we present an analysis of
examples from the literature where building communicative capital has enabled a
progression of fruitful, task-directed interactions between prostheses and
their human users. We then describe further work that is needed to concretely
evaluate the hypothesis that prostheses are best thought of as agents. The
agent-based viewpoint developed in this article significantly extends current
thinking on how best to support the natural, functional use of increasingly
complex prosthetic enhancements, and opens the door for more powerful
interactions between humans and their assistive technologies. | [
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Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results. | [
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] |
Mobile network that millions of people use every day is one of the most
complex systems in the world. Optimization of mobile network to meet exploding
customer demand and reduce capital/operation expenditures poses great
challenges. Despite recent progress, application of deep reinforcement learning
(DRL) to complex real world problem still remains unsolved, given data
scarcity, partial observability, risk and complex rules/dynamics in real world,
as well as the huge reality gap between simulation and real world. To bridge
the reality gap, we introduce a Sim-to-Real framework to directly transfer
learning from simulation to real world via graph convolutional neural network
(CNN) - by abstracting partially observable mobile network into graph, then
distilling domain-variant irregular graph into domain-invariant tensor in
locally Euclidean space as input to CNN -, domain randomization and multi-task
learning. We use a novel self-play mechanism to encourage competition among DRL
agents for best record on multiple tasks via simulated annealing, just like
athletes compete for world record in decathlon. We also propose a decentralized
multi-agent, competitive and cooperative DRL method to coordinate the actions
of multi-cells to maximize global reward and minimize negative impact to
neighbor cells. Using 6 field trials on commercial mobile networks, we
demonstrate for the first time that a DRL agent can successfully transfer
learning from simulation to complex real world problem with imperfect
information, complex rules/dynamics, huge state/action space, and multi-agent
interactions, without any training in the real world. | [
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] |
Simulation-to-real transfer is an important strategy for making reinforcement
learning practical with real robots. Successful sim-to-real transfer systems
have difficulty producing policies which generalize across tasks, despite
training for thousands of hours equivalent real robot time. To address this
shortcoming, we present a novel approach to efficiently learning new robotic
skills directly on a real robot, based on model-predictive control (MPC) and an
algorithm for learning task representations. In short, we show how to reuse the
simulation from the pre-training step of sim-to-real methods as a tool for
foresight, allowing the sim-to-real policy adapt to unseen tasks. Rather than
end-to-end learning policies for single tasks and attempting to transfer them,
we first use simulation to simultaneously learn (1) a continuous
parameterization (i.e. a skill embedding or latent) of task-appropriate
primitive skills, and (2) a single policy for these skills which is conditioned
on this representation. We then directly transfer our multi-skill policy to a
real robot, and actuate the robot by choosing sequences of skill latents which
actuate the policy, with each latent corresponding to a pre-learned primitive
skill controller. We complete unseen tasks by choosing new sequences of skill
latents to control the robot using MPC, where our MPC model is composed of the
pre-trained skill policy executed in the simulation environment, run in
parallel with the real robot. We discuss the background and principles of our
method, detail its practical implementation, and evaluate its performance by
using our method to train a real Sawyer Robot to achieve motion tasks such as
drawing and block pushing. | [
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Diabetes is a major public health problem in the United States, affecting
roughly 30 million people. Diabetes complications, along with the mental health
comorbidities that often co-occur with them, are major drivers of high
healthcare costs, poor outcomes, and reduced treatment adherence in diabetes.
Here, we evaluate in a large state-wide population whether we can use
artificial intelligence (AI) techniques to identify clusters of patient
trajectories within the broader diabetes population in order to create
cost-effective, narrowly-focused case management intervention strategies to
reduce development of complications. This approach combined data from: 1)
claims, 2) case management notes, and 3) social determinants of health from
~300,000 real patients between 2014 and 2016. We categorized complications as
five types: Cardiovascular, Neuropathy, Opthalmic, Renal, and Other. Modeling
was performed combining a variety of machine learning algorithms, including
supervised classification, unsupervised clustering, natural language processing
of unstructured care notes, and feature engineering. The results showed that we
can predict development of diabetes complications roughly 83.5% of the time
using claims data or social determinants of health data. They also showed we
can reveal meaningful clusters in the patient population related to
complications and mental health that can be used to cost-effective screening
program, reducing the number of patients to be screened down by 85%. This study
outlines creation of an AI framework to develop protocols to better address
mental health comorbidities that lead to complications development in the
diabetes population. Future work is described that outlines potential lines of
research and the need for better addressing the 'people side' of the equation. | [
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] |
Smart services are an important element of the smart cities and the Internet
of Things (IoT) ecosystems where the intelligence behind the services is
obtained and improved through the sensory data. Providing a large amount of
training data is not always feasible; therefore, we need to consider
alternative ways that incorporate unlabeled data as well. In recent years, Deep
reinforcement learning (DRL) has gained great success in several application
domains. It is an applicable method for IoT and smart city scenarios where
auto-generated data can be partially labeled by users' feedback for training
purposes. In this paper, we propose a semi-supervised deep reinforcement
learning model that fits smart city applications as it consumes both labeled
and unlabeled data to improve the performance and accuracy of the learning
agent. The model utilizes Variational Autoencoders (VAE) as the inference
engine for generalizing optimal policies. To the best of our knowledge, the
proposed model is the first investigation that extends deep reinforcement
learning to the semi-supervised paradigm. As a case study of smart city
applications, we focus on smart buildings and apply the proposed model to the
problem of indoor localization based on BLE signal strength. Indoor
localization is the main component of smart city services since people spend
significant time in indoor environments. Our model learns the best action
policies that lead to a close estimation of the target locations with an
improvement of 23% in terms of distance to the target and at least 67% more
received rewards compared to the supervised DRL model. | [
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] |
Click-through rate (CTR) prediction, which aims to predict the probability of
a user clicking on an ad or an item, is critical to many online applications
such as online advertising and recommender systems. The problem is very
challenging since (1) the input features (e.g., the user id, user age, item id,
item category) are usually sparse and high-dimensional, and (2) an effective
prediction relies on high-order combinatorial features (\textit{a.k.a.} cross
features), which are very time-consuming to hand-craft by domain experts and
are impossible to be enumerated. Therefore, there have been efforts in finding
low-dimensional representations of the sparse and high-dimensional raw features
and their meaningful combinations. In this paper, we propose an effective and
efficient method called the \emph{AutoInt} to automatically learn the
high-order feature interactions of input features. Our proposed algorithm is
very general, which can be applied to both numerical and categorical input
features. Specifically, we map both the numerical and categorical features into
the same low-dimensional space. Afterwards, a multi-head self-attentive neural
network with residual connections is proposed to explicitly model the feature
interactions in the low-dimensional space. With different layers of the
multi-head self-attentive neural networks, different orders of feature
combinations of input features can be modeled. The whole model can be
efficiently fit on large-scale raw data in an end-to-end fashion. Experimental
results on four real-world datasets show that our proposed approach not only
outperforms existing state-of-the-art approaches for prediction but also offers
good explainability. Code is available at:
\url{https://github.com/DeepGraphLearning/RecommenderSystems}. | [
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Illegal wildlife poaching threatens ecosystems and drives endangered species
toward extinction. However, efforts for wildlife protection are constrained by
the limited resources of law enforcement agencies. To help combat poaching, the
Protection Assistant for Wildlife Security (PAWS) is a machine learning
pipeline that has been developed as a data-driven approach to identify areas at
high risk of poaching throughout protected areas and compute optimal patrol
routes. In this paper, we take an end-to-end approach to the data-to-deployment
pipeline for anti-poaching. In doing so, we address challenges including
extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife
poaching data to enhance PAWS, and we apply our methodology to three national
parks with diverse characteristics. (i) We use Gaussian processes to quantify
predictive uncertainty, which we exploit to improve robustness of our
prescribed patrols and increase detection of snares by an average of 30%. We
evaluate our approach on real-world historical poaching data from Murchison
Falls and Queen Elizabeth National Parks in Uganda and, for the first time,
Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of
large-scale field tests conducted in Murchison Falls and Srepok Wildlife
Sanctuary which confirm that the predictive power of PAWS extends promisingly
to multiple parks. This paper is part of an effort to expand PAWS to 800 parks
around the world through integration with SMART conservation software. | [
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] |
To reduce training time of large-scale DNNs, scientists have started to
explore parallelization strategies like data-parallelism, model-parallelism,
and hybrid-parallelism. While data-parallelism has been extensively studied and
developed, several problems exist in realizing model-parallelism and
hybrid-parallelism efficiently. Four major problems we focus on are: 1)
defining a notion of a distributed model across processes, 2) implementing
forward/back-propagation across process boundaries that requires explicit
communication, 3) obtaining parallel speedup on an inherently sequential task,
and 4) achieving scalability without losing out on a model's accuracy. To
address these problems, we create HyPar-Flow --- a model-size/-type agnostic,
scalable, practical, and user-transparent system for hybrid-parallel training
by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that
can be used to perform data, model, and hybrid parallel training of any Keras
model at scale. We create an internal distributed representation of the
user-provided Keras model, utilize TF's Eager execution features for
distributed forward/back-propagation across processes, exploit pipelining to
improve performance and leverage efficient MPI primitives for scalable
communication. Between model partitions, we use send and recv to exchange
layer-data/partial-errors while allreduce is used to accumulate/average
gradients across model replicas. Beyond the design and implementation of
HyPar-Flow, we also provide comprehensive correctness and performance results
on three state-of-the-art HPC systems including TACC Frontera (#5 on
Top500.org). For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up
to 1.6x speedup over Horovod-based data-parallel training, 2) 110x speedup over
single-node on 128 Stampede2 nodes, and 3) 481x speedup over single-node on 512
Frontera nodes. | [
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] |
Deep learning methods, which have found successful applications in fields
like image classification and natural language processing, have recently been
applied to source code analysis too, due to the enormous amount of freely
available source code (e.g., from open-source software repositories).
In this work, we elaborate upon a state-of-the-art approach to the
representation of source code that uses information about its syntactic
structure, and we adapt it to represent source changes (i.e., commits). We use
this representation to classify security-relevant commits.
Because our method uses transfer learning (that is, we train a network on a
"pretext task" for which abundant labeled data is available, and then we use
such network for the target task of commit classification, for which fewer
labeled instances are available), we studied the impact of pre-training the
network using two different pretext tasks versus a randomly initialized model.
Our results indicate that representations that leverage the structural
information obtained through code syntax outperform token-based
representations. Furthermore, the performance metrics obtained when
pre-training on a loosely related pretext task with a very large dataset
($>10^6$ samples) were surpassed when pretraining on a smaller dataset ($>10^4$
samples) but for a pretext task that is more closely related to the target
task. | [
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Machine learning technologies are increasingly developed for use in
healthcare. While research communities have focused on creating
state-of-the-art models, there has been less focus on real world implementation
and the associated challenges to accuracy, fairness, accountability, and
transparency that come from actual, situated use. Serious questions remain
under examined regarding how to ethically build models, interpret and explain
model output, recognize and account for biases, and minimize disruptions to
professional expertise and work cultures. We address this gap in the literature
and provide a detailed case study covering the development, implementation, and
evaluation of Sepsis Watch, a machine learning-driven tool that assists
hospital clinicians in the early diagnosis and treatment of sepsis. We, the
team that developed and evaluated the tool, discuss our conceptualization of
the tool not as a model deployed in the world but instead as a socio-technical
system requiring integration into existing social and professional contexts.
Rather than focusing on model interpretability to ensure a fair and accountable
machine learning, we point toward four key values and practices that should be
considered when developing machine learning to support clinical
decision-making: rigorously define the problem in context, build relationships
with stakeholders, respect professional discretion, and create ongoing feedback
loops with stakeholders. Our work has significant implications for future
research regarding mechanisms of institutional accountability and
considerations for designing machine learning systems. Our work underscores the
limits of model interpretability as a solution to ensure transparency,
accuracy, and accountability in practice. Instead, our work demonstrates other
means and goals to achieve FATML values in design and in practice. | [
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] |
The ability to explain decisions made by AI systems is highly sought after,
especially in domains where human lives are at stake such as medicine or
autonomous vehicles. While it is often possible to approximate the input-output
relations of deep neural networks with a few human-understandable rules, the
discovery of the double descent phenomena suggests that such approximations do
not accurately capture the mechanism by which deep neural networks work. Double
descent indicates that deep neural networks typically operate by smoothly
interpolating between data points rather than by extracting a few high level
rules. As a result, neural networks trained on complex real world data are
inherently hard to interpret and prone to failure if asked to extrapolate. To
show how we might be able to trust AI despite these problems we introduce the
concept of self-explaining AI. Self-explaining AIs are capable of providing a
human-understandable explanation of each decision along with confidence levels
for both the decision and explanation. For this approach to work, it is
important that the explanation actually be related to the decision, ideally
capturing the mechanism used to arrive at the explanation. Finally, we argue it
is important that deep learning based systems include a "warning light" based
on techniques from applicability domain analysis to warn the user if a model is
asked to extrapolate outside its training distribution. For a video
presentation of this talk see https://www.youtube.com/watch?v=Py7PVdcu7WY& . | [
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] |
We address the problem of finding a minimal separator in an
Andersson-Madigan-Perlman chain graph (AMP CG), namely, finding a set Z of
nodes that separates a given nonadjacent pair of nodes such that no proper
subset of Z separates that pair. We analyze several versions of this problem
and offer polynomial-time algorithms for each. These include finding a minimal
separator from a restricted set of nodes, finding a minimal separator for two
given disjoint sets, and testing whether a given separator is minimal. To
address the problem of learning the structure of AMP CGs from data, we show
that the PC-like algorithm (Pena, 2012) is order-dependent, in the sense that
the output can depend on the order in which the variables are given. We propose
several modifications of the PC-like algorithm that remove part or all of this
order-dependence. We also extend the decomposition-based approach for learning
Bayesian networks (BNs) proposed by (Xie et al., 2006) to learn AMP CGs, which
include BNs as a special case, under the faithfulness assumption. We prove the
correctness of our extension using the minimal separator results. Using
standard benchmarks and synthetically generated models and data in our
experiments demonstrate the competitive performance of our decomposition-based
method, called LCD-AMP, in comparison with the (modified versions of) PC-like
algorithm. The LCD-AMP algorithm usually outperforms the PC-like algorithm, and
our modifications of the PC-like algorithm learn structures that are more
similar to the underlying ground truth graphs than the original PC-like
algorithm, especially in high-dimensional settings. In particular, we
empirically show that the results of both algorithms are more accurate and
stabler when the sample size is reasonably large and the underlying graph is
sparse. | [
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Due to the availability of huge amounts of data and processing abilities,
current artificial intelligence (AI) systems are effective in solving complex
tasks. However, despite the success of AI in different areas, the problem of
designing AI systems that can truly mimic human cognitive capabilities such as
artificial general intelligence, remains largely open. Consequently, many
emerging cross-device AI applications will require a transition from
traditional centralized learning systems towards large-scale distributed AI
systems that can collaboratively perform multiple complex learning tasks. In
this paper, we propose a novel design philosophy called democratized learning
(Dem-AI) whose goal is to build large-scale distributed learning systems that
rely on the self-organization of distributed learning agents that are
well-connected, but limited in learning capabilities. Correspondingly, inspired
by the societal groups of humans, the specialized groups of learning agents in
the proposed Dem-AI system are self-organized in a hierarchical structure to
collectively perform learning tasks more efficiently. As such, the Dem-AI
learning system can evolve and regulate itself based on the underlying duality
of two processes which we call specialized and generalized processes. In this
regard, we present a reference design as a guideline to realize future Dem-AI
systems, inspired by various interdisciplinary fields. Accordingly, we
introduce four underlying mechanisms in the design such as plasticity-stability
transition mechanism, self-organizing hierarchical structuring, specialized
learning, and generalization. Finally, we establish possible extensions and new
challenges for the existing learning approaches to provide better scalable,
flexible, and more powerful learning systems with the new setting of Dem-AI. | [
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] |
Knowledge graphs (KGs) store highly heterogeneous information about the world
in the structure of a graph, and are useful for tasks such as question
answering and reasoning. However, they often contain errors and are missing
information. Vibrant research in KG refinement has worked to resolve these
issues, tailoring techniques to either detect specific types of errors or
complete a KG.
In this work, we introduce a unified solution to KG characterization by
formulating the problem as unsupervised KG summarization with a set of
inductive, soft rules, which describe what is normal in a KG, and thus can be
used to identify what is abnormal, whether it be strange or missing. Unlike
first-order logic rules, our rules are labeled, rooted graphs, i.e., patterns
that describe the expected neighborhood around a (seen or unseen) node, based
on its type, and information in the KG. Stepping away from the traditional
support/confidence-based rule mining techniques, we propose KGist, Knowledge
Graph Inductive SummarizaTion, which learns a summary of inductive rules that
best compress the KG according to the Minimum Description Length principle---a
formulation that we are the first to use in the context of KG rule mining. We
apply our rules to three large KGs (NELL, DBpedia, and Yago), and tasks such as
compression, various types of error detection, and identification of incomplete
information. We show that KGist outperforms task-specific, supervised and
unsupervised baselines in error detection and incompleteness identification,
(identifying the location of up to 93% of missing entities---over 10% more than
baselines), while also being efficient for large knowledge graphs. | [
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] |
Debate about fairness in machine learning has largely centered around
competing definitions of what fairness or nondiscrimination between groups
requires. However, little attention has been paid to what precisely a group is.
Many recent approaches to "fairness" require one to specify a causal model of
the data generating process. These exercises make an implicit ontological
assumption that a racial or sex group is simply a collection of individuals who
share a given trait. We show this by exploring the formal assumption of
modularity in causal models, which holds that the dependencies captured by one
causal pathway are invariant to interventions on any other pathways. Causal
models of sex propose two substantive claims: 1) There exists a feature,
sex-on-its-own, that is an inherent trait of an individual that causally brings
about social phenomena external to it in the world; and 2) the relations
between sex and its effects can be modified in whichever ways and the former
feature would still retain the meaning that sex has in our world. We argue that
this ontological picture is false. Many of the "effects" that sex purportedly
"causes" are in fact constitutive features of sex as a social status. They give
the social meaning of sex features, meanings that are precisely what make sex
discrimination a distinctively morally problematic type of action. Correcting
this conceptual error has a number of implications for how models can be used
to detect discrimination. Formal diagrams of constitutive relations present an
entirely different path toward reasoning about discrimination. Whereas causal
diagrams guide the construction of sophisticated modular counterfactuals,
constitutive diagrams identify a different kind of counterfactual as central to
an inquiry on discrimination: one that asks how the social meaning of a group
would be changed if its non-modular features were altered. | [
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As artificial intelligence becomes increasingly intelligent---in some cases,
achieving superhuman performance---there is growing potential for humans to
learn from and collaborate with algorithms. However, the ways in which AI
systems approach problems are often different from the ways people do, and thus
may be uninterpretable and hard to learn from. A crucial step in bridging this
gap between human and artificial intelligence is modeling the granular actions
that constitute human behavior, rather than simply matching aggregate human
performance.
We pursue this goal in a model system with a long history in artificial
intelligence: chess. The aggregate performance of a chess player unfolds as
they make decisions over the course of a game. The hundreds of millions of
games played online by players at every skill level form a rich source of data
in which these decisions, and their exact context, are recorded in minute
detail. Applying existing chess engines to this data, including an open-source
implementation of AlphaZero, we find that they do not predict human moves well.
We develop and introduce Maia, a customized version of Alpha-Zero trained on
human chess games, that predicts human moves at a much higher accuracy than
existing engines, and can achieve maximum accuracy when predicting decisions
made by players at a specific skill level in a tuneable way. For a dual task of
predicting whether a human will make a large mistake on the next move, we
develop a deep neural network that significantly outperforms competitive
baselines. Taken together, our results suggest that there is substantial
promise in designing artificial intelligence systems with human collaboration
in mind by first accurately modeling granular human decision-making. | [
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] |
Analogy-making is at the core of human and artificial intelligence and
creativity with applications to such diverse tasks as proving mathematical
theorems and building mathematical theories, common sense reasoning, learning,
language acquisition, and story telling. This paper introduces from first
principles an abstract algebraic framework of analogical proportions of the
form `$a$ is to $b$ what $c$ is to $d$' in the general setting of universal
algebra. This enables us to compare mathematical objects possibly across
different domains in a uniform way which is crucial for AI-systems. It turns
out that our notion of analogical proportions has appealing mathematical
properties. As we construct our model from first principles using only
elementary concepts of universal algebra, and since our model questions some
basic properties of analogical proportions presupposed in the literature, to
convince the reader of the plausibility of our model we show that it can be
naturally embedded into first-order logic via model-theoretic types and prove
from that perspective that analogical proportions are compatible with
structure-preserving mappings. This provides conceptual evidence for its
applicability. In a broader sense, this paper is a first step towards a theory
of analogical reasoning and learning systems with potential applications to
fundamental AI-problems like common sense reasoning and computational learning
and creativity. | [
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