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The exercise of detecting similar bug reports in bug tracking systems is
known as duplicate bug report detection. Having prior knowledge of a bug
report's existence reduces efforts put into debugging problems and identifying
the root cause. Rule and Query-based solutions recommend a long list of
potential similar bug reports with no clear ranking. In addition, triage
engineers are less motivated to spend time going through an extensive list.
Consequently, this deters the use of duplicate bug report retrieval solutions.
In this paper, we have proposed a solution using a combination of NLP
techniques. Our approach considers unstructured and structured attributes of a
bug report like summary, description and severity, impacted products,
platforms, categories, etc. It uses a custom data transformer, a deep neural
network, and a non-generalizing machine learning method to retrieve existing
identical bug reports. We have performed numerous experiments with significant
data sources containing thousands of bug reports and showcased that the
proposed solution achieves a high retrieval accuracy of 70% for recall@5. | [
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] |
The rapid adoption of residential solar photovoltaics (PV) has resulted in
regular overvoltage events, due to correlated reverse power flows. Currently,
PV inverters prevent damage to electronics by curtailing energy production in
response to overvoltage. However, this disproportionately affects households at
the far end of the feeder, leading to an unfair allocation of the potential
value of energy produced. Globally optimizing for fair curtailment requires
accurate feeder parameters, which are often unknown. This paper investigates
reinforcement learning, which gradually optimizes a fair PV curtailment
strategy by interacting with the system. We evaluate six fairness metrics on
how well they can be learned compared to an optimal solution oracle. We show
that all definitions permit efficient learning, suggesting that reinforcement
learning is a promising approach to achieving both safe and fair PV
coordination. | [
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] |
Navigation is one of the most heavily studied problems in robotics, and is
conventionally approached as a geometric mapping and planning problem. However,
real-world navigation presents a complex set of physical challenges that defies
simple geometric abstractions. Machine learning offers a promising way to go
beyond geometry and conventional planning, allowing for navigational systems
that make decisions based on actual prior experience. Such systems can reason
about traversability in ways that go beyond geometry, accounting for the
physical outcomes of their actions and exploiting patterns in real-world
environments. They can also improve as more data is collected, potentially
providing a powerful network effect. In this article, we present a general
toolkit for experiential learning of robotic navigation skills that unifies
several recent approaches, describe the underlying design principles, summarize
experimental results from several of our recent papers, and discuss open
problems and directions for future work. | [
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] |
With the growing need to reduce energy consumption and greenhouse gas
emissions, Eco-driving strategies provide a significant opportunity for
additional fuel savings on top of other technological solutions being pursued
in the transportation sector. In this paper, a model-free deep reinforcement
learning (RL) control agent is proposed for active Eco-driving assistance that
trades-off fuel consumption against other driver-accommodation objectives, and
learns optimal traction torque and transmission shifting policies from
experience. The training scheme for the proposed RL agent uses an off-policy
actor-critic architecture that iteratively does policy evaluation with a
multi-step return and policy improvement with the maximum posteriori policy
optimization algorithm for hybrid action spaces. The proposed Eco-driving RL
agent is implemented on a commercial vehicle in car following traffic. It shows
superior performance in minimizing fuel consumption compared to a baseline
controller that has full knowledge of fuel-efficiency tables. | [
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] |
Eco-driving strategies have been shown to provide significant reductions in
fuel consumption. This paper outlines an active driver assistance approach that
uses a residual policy learning (RPL) agent trained to provide residual actions
to default power train controllers while balancing fuel consumption against
other driver-accommodation objectives. Using previous experiences, our RPL
agent learns improved traction torque and gear shifting residual policies to
adapt the operation of the powertrain to variations and uncertainties in the
environment. For comparison, we consider a traditional reinforcement learning
(RL) agent trained from scratch. Both agents employ the off-policy Maximum A
Posteriori Policy Optimization algorithm with an actor-critic architecture. By
implementing on a simulated commercial vehicle in various car-following
scenarios, we find that the RPL agent quickly learns significantly improved
policies compared to a baseline source policy but in some measures not as good
as those eventually possible with the RL agent trained from scratch. | [
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] |
In this paper, we consider the inventory management (IM) problem where we
need to make replenishment decisions for a large number of stock keeping units
(SKUs) to balance their supply and demand. In our setting, the constraint on
the shared resources (such as the inventory capacity) couples the otherwise
independent control for each SKU. We formulate the problem with this structure
as Shared-Resource Stochastic Game (SRSG)and propose an efficient algorithm
called Context-aware Decentralized PPO (CD-PPO). Through extensive experiments,
we demonstrate that CD-PPO can accelerate the learning procedure compared with
standard MARL algorithms. | [
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] |
The automated synthesis of correct-by-construction Boolean functions from
logical specifications is known as the Boolean Functional Synthesis (BFS)
problem. BFS has many application areas that range from software engineering to
circuit design. In this paper, we introduce a tool BNSynth, that is the first
to solve the BFS problem under a given bound on the solution space. Bounding
the solution space induces the synthesis of smaller functions that benefit
resource constrained areas such as circuit design. BNSynth uses a
counter-example guided, neural approach to solve the bounded BFS problem.
Initial results show promise in synthesizing smaller solutions; we observe at
least \textbf{3.2X} (and up to \textbf{24X}) improvement in the reduction of
solution size on average, as compared to state of the art tools on our
benchmarks. BNSynth is available on GitHub under an open source license. | [
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] |
While reinforcement learning has shown experimental success in a number of
applications, it is known to be sensitive to noise and perturbations in the
parameters of the system, leading to high variance in the total reward amongst
different episodes on slightly different environments. To introduce robustness,
as well as sample efficiency, risk-sensitive reinforcement learning methods are
being thoroughly studied. In this work, we provide a definition of robust
reinforcement learning policies and formulate a risk-sensitive reinforcement
learning problem to approximate them, by solving an optimization problem with
respect to a modified objective based on exponential criteria. In particular,
we study a model-free risk-sensitive variation of the widely-used Monte Carlo
Policy Gradient algorithm, and introduce a novel risk-sensitive online
Actor-Critic algorithm based on solving a multiplicative Bellman equation using
stochastic approximation updates. Analytical results suggest that the use of
exponential criteria generalizes commonly used ad-hoc regularization
approaches, improves sample efficiency, and introduces robustness with respect
to perturbations in the model parameters and the environment. The
implementation, performance, and robustness properties of the proposed methods
are evaluated in simulated experiments. | [
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] |
Understanding the power and limitations of quantum access to data in machine
learning tasks is primordial to assess the potential of quantum computing in
artificial intelligence. Previous works have already shown that speed-ups in
learning are possible when given quantum access to reinforcement learning
environments. Yet, the applicability of quantum algorithms in this setting
remains very limited, notably in environments with large state and action
spaces. In this work, we design quantum algorithms to train state-of-the-art
reinforcement learning policies by exploiting quantum interactions with an
environment. However, these algorithms only offer full quadratic speed-ups in
sample complexity over their classical analogs when the trained policies
satisfy some regularity conditions. Interestingly, we find that reinforcement
learning policies derived from parametrized quantum circuits are well-behaved
with respect to these conditions, which showcases the benefit of a
fully-quantum reinforcement learning framework. | [
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] |
Many real-world reinforcement learning tasks require control of complex
dynamical systems that involve both costly data acquisition processes and large
state spaces. In cases where the transition dynamics can be readily evaluated
at specified states (e.g., via a simulator), agents can operate in what is
often referred to as planning with a \emph{generative model}. We propose the
AE-LSVI algorithm for best-policy identification, a novel variant of the
kernelized least-squares value iteration (LSVI) algorithm that combines
optimism with pessimism for active exploration (AE). AE-LSVI provably
identifies a near-optimal policy \emph{uniformly} over an entire state space
and achieves polynomial sample complexity guarantees that are independent of
the number of states. When specialized to the recently introduced offline
contextual Bayesian optimization setting, our algorithm achieves improved
sample complexity bounds. Experimentally, we demonstrate that AE-LSVI
outperforms other RL algorithms in a variety of environments when robustness to
the initial state is required. | [
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] |
There is significant interest in deploying machine learning algorithms for
diagnostic radiology, as modern learning techniques have made it possible to
detect abnormalities in medical images within minutes. While machine-assisted
diagnoses cannot yet reliably replace human reviews of images by a radiologist,
they could inform prioritization rules for determining the order by which to
review patient cases so that patients with time-sensitive conditions could
benefit from early intervention.
We study this scenario by formulating it as a learning-augmented online
scheduling problem. We are given information about each arriving patient's
urgency level in advance, but these predictions are inevitably error-prone. In
this formulation, we face the challenges of decision making under imperfect
information, and of responding dynamically to prediction error as we observe
better data in real-time. We propose a simple online policy and show that this
policy is in fact the best possible in certain stylized settings. We also
demonstrate that our policy achieves the two desiderata of online algorithms
with predictions: consistency (performance improvement with prediction
accuracy) and robustness (protection against the worst case). We complement our
theoretical findings with empirical evaluations of the policy under settings
that more accurately reflect clinical scenarios in the real world. | [
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] |
Learning to rank is an effective recommendation approach since its
introduction around 2010. Famous algorithms such as Bayesian Personalized
Ranking and Collaborative Less is More Filtering have left deep impact in both
academia and industry. However, most learning to rank approaches focus on
improving technical accuracy metrics such as AUC, MRR and NDCG. Other
evaluation metrics of recommender systems like fairness have been largely
overlooked until in recent years. In this paper, we propose a new learning to
rank algorithm named Pareto Pairwise Ranking. We are inspired by the idea of
Bayesian Personalized Ranking and power law distribution. We show that our
algorithm is competitive with other algorithms when evaluated on technical
accuracy metrics. What is more important, in our experiment section we
demonstrate that Pareto Pairwise Ranking is the most fair algorithm in
comparison with 9 other contemporary algorithms. | [
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] |
A computational graph in a deep neural network (DNN) denotes a specific data
flow diagram (DFD) composed of many tensors and operators. Existing toolkits
for visualizing computational graphs are not applicable when the structure is
highly complicated and large-scale (e.g., BERT [1]). To address this problem,
we propose leveraging a suite of visual simplification techniques, including a
cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic
subgraph stacking strategy. We design and implement an interactive
visualization system that is suitable for computational graphs with up to 10
thousand elements. Experimental results and usage scenarios demonstrate that
our tool reduces 60% elements on average and hence enhances the performance for
recognizing and diagnosing DNN models. Our contributions are integrated into an
open-source DNN visualization toolkit, namely, MindInsight [2]. | [
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] |
We address the problem of unsupervised domain adaptation when the source
domain differs from the target domain because of a shift in the distribution of
a latent subgroup. When this subgroup confounds all observed data, neither
covariate shift nor label shift assumptions apply. We show that the optimal
target predictor can be non-parametrically identified with the help of concept
and proxy variables available only in the source domain, and unlabeled data
from the target. The identification results are constructive, immediately
suggesting an algorithm for estimating the optimal predictor in the target. For
continuous observations, when this algorithm becomes impractical, we propose a
latent variable model specific to the data generation process at hand. We show
how the approach degrades as the size of the shift changes, and verify that it
outperforms both covariate and label shift adjustment. | [
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This paper presents a methodology for integrating machine learning techniques
into metaheuristics for solving combinatorial optimization problems. Namely, we
propose a general machine learning framework for neighbor generation in
metaheuristic search. We first define an efficient neighborhood structure
constructed by applying a transformation to a selected subset of variables from
the current solution. Then, the key of the proposed methodology is to generate
promising neighbors by selecting a proper subset of variables that contains a
descent of the objective in the solution space. To learn a good variable
selection strategy, we formulate the problem as a classification task that
exploits structural information from the characteristics of the problem and
from high-quality solutions. We validate our methodology on two metaheuristic
applications: a Tabu Search scheme for solving a Wireless Network Optimization
problem and a Large Neighborhood Search heuristic for solving Mixed-Integer
Programs. The experimental results show that our approach is able to achieve a
satisfactory trade-off between the exploration of a larger solution space and
the exploitation of high-quality solution regions on both applications. | [
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] |
Deep Reinforcement Learning is emerging as a promising approach for the
continuous control task of robotic arm movement. However, the challenges of
learning robust and versatile control capabilities are still far from being
resolved for real-world applications, mainly because of two common issues of
this learning paradigm: the exploration strategy and the slow learning speed,
sometimes known as "the curse of dimensionality". This work aims at exploring
and assessing the advantages of the application of Quantum Computing to one of
the state-of-art Reinforcement Learning techniques for continuous control -
namely Soft Actor-Critic. Specifically, the performance of a Variational
Quantum Soft Actor-Critic on the movement of a virtual robotic arm has been
investigated by means of digital simulations of quantum circuits. A quantum
advantage over the classical algorithm has been found in terms of a significant
decrease in the amount of required parameters for satisfactory model training,
paving the way for further promising developments. | [
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] |
We propose a neural network-based model for nonlinear dynamics in continuous
time that can impose inductive biases on decay rates and/or frequencies.
Inductive biases are helpful for training neural networks especially when
training data are small. The proposed model is based on the Koopman operator
theory, where the decay rate and frequency information is used by restricting
the eigenvalues of the Koopman operator that describe linear evolution in a
Koopman space. We use neural networks to find an appropriate Koopman space,
which are trained by minimizing multi-step forecasting and backcasting errors
using irregularly sampled time-series data. Experiments on various time-series
datasets demonstrate that the proposed method achieves higher forecasting
performance given a single short training sequence than the existing methods. | [
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] |
Three main points: 1. Data Science (DS) will be increasingly important to
heliophysics; 2. Methods of heliophysics science discovery will continually
evolve, requiring the use of learning technologies [e.g., machine learning
(ML)] that are applied rigorously and that are capable of supporting discovery;
and 3. To grow with the pace of data, technology, and workforce changes,
heliophysics requires a new approach to the representation of knowledge. | [
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] |
Reduced system dependability and higher maintenance costs may be the
consequence of poor electric power quality, which can disturb normal equipment
performance, speed up aging, and even cause outright failures. This study
implements and tests a prototype of an Online Sequential Extreme Learning
Machine (OS-ELM) classifier based on wavelets for detecting power quality
problems under transient conditions. In order to create the classifier, the
OSELM-network model and the discrete wavelet transform (DWT) method are
combined. First, discrete wavelet transform (DWT) multi-resolution analysis
(MRA) was used to extract characteristics of the distorted signal at various
resolutions. The OSELM then sorts the retrieved data by transient duration and
energy features to determine the kind of disturbance. The suggested approach
requires less memory space and processing time since it can minimize a large
quantity of the distorted signal's characteristics without changing the
signal's original quality. Several types of transient events were used to
demonstrate the classifier's ability to detect and categorize various types of
power disturbances, including sags, swells, momentary interruptions,
oscillatory transients, harmonics, notches, spikes, flickers, sag swell, sag
mi, sag harm, swell trans, sag spike, and swell spike. | [
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] |
With the devastating outbreak of COVID-19, vaccines are one of the crucial
lines of defense against mass infection in this global pandemic. Given the
protection they provide, vaccines are becoming mandatory in certain social and
professional settings. This paper presents a classification model for detecting
COVID-19 vaccination related search queries, a machine learning model that is
used to generate search insights for COVID-19 vaccinations. The proposed method
combines and leverages advancements from modern state-of-the-art (SOTA) natural
language understanding (NLU) techniques such as pretrained Transformers with
traditional dense features. We propose a novel approach of considering dense
features as memory tokens that the model can attend to. We show that this new
modeling approach enables a significant improvement to the Vaccine Search
Insights (VSI) task, improving a strong well-established gradient-boosting
baseline by relative +15% improvement in F1 score and +14% in precision. | [
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] |
In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns,
advertisers always run the risk of spending the budget without getting enough
conversions. Moreover, the bidding on advertising inventory has few connections
with propensity one that can reach to target cost-per-acquisition (tCPA) goals.
To address this problem, this paper presents a bid optimization scenario to
achieve the desired tCPA goals for advertisers. In particular, we build the
optimization engine to make a decision by solving the rigorously formalized
constrained optimization problem, which leverages the bid landscape model
learned from rich historical auction data using non-parametric learning. The
proposed model can naturally recommend the bid that meets the advertisers'
expectations by making inference over advertisers' historical auction
behaviors, which essentially deals with the data challenges commonly faced by
bid landscape modeling: incomplete logs in auctions, and uncertainty due to the
variation and fluctuations in advertising bidding behaviors. The bid
optimization model outperforms the baseline methods on real-world campaigns,
and has been applied into a wide range of scenarios for performance improvement
and revenue liftup. | [
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] |
Machine Learning-as-a-Service systems (MLaaS) have been largely developed for
cybersecurity-critical applications, such as detecting network intrusions and
fake news campaigns. Despite effectiveness, their robustness against
adversarial attacks is one of the key trust concerns for MLaaS deployment. We
are thus motivated to assess the adversarial robustness of the Machine Learning
models residing at the core of these security-critical applications with
categorical inputs. Previous research efforts on accessing model robustness
against manipulation of categorical inputs are specific to use cases and
heavily depend on domain knowledge, or require white-box access to the target
ML model. Such limitations prevent the robustness assessment from being as a
domain-agnostic service provided to various real-world applications. We propose
a provably optimal yet computationally highly efficient adversarial robustness
assessment protocol for a wide band of ML-driven cybersecurity-critical
applications. We demonstrate the use of the domain-agnostic robustness
assessment method with substantial experimental study on fake news detection
and intrusion detection problems. | [
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] |
The secondary structure of ribonucleic acid (RNA) is more stable and
accessible in the cell than its tertiary structure, making it essential for
functional prediction. Although deep learning has shown promising results in
this field, current methods suffer from poor generalization and high
complexity. In this work, we present RFold, a simple yet effective RNA
secondary structure prediction in an end-to-end manner. RFold introduces a
decoupled optimization process that decomposes the vanilla constraint
satisfaction problem into row-wise and column-wise optimization, simplifying
the solving process while guaranteeing the validity of the output. Moreover,
RFold adopts attention maps as informative representations instead of designing
hand-crafted features. Extensive experiments demonstrate that RFold achieves
competitive performance and about eight times faster inference efficiency than
the state-of-the-art method. The code and Colab demo are available in
\href{http://github.com/A4Bio/RFold}{http://github.com/A4Bio/RFold}. | [
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] |
Accurately predicting their future performance can ensure students successful
graduation, and help them save both time and money. However, achieving such
predictions faces two challenges, mainly due to the diversity of students'
background and the necessity of continuously tracking their evolving progress.
The goal of this work is to create a system able to automatically detect
students in difficulty, for instance predicting if they are likely to fail a
course. We compare a naive approach widely used in the literature, which uses
attributes available in the data set (like the grades), with a personalized
approach we called Personalized Student Attribute Inference (PSAI). With our
model, we create personalized attributes to capture the specific background of
each student. Both approaches are compared using machine learning algorithms
like decision trees, support vector machine or neural networks. | [
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] |
In computer-aided education and intelligent tutoring systems, knowledge
tracing (KT) raises attention due to the development of data-driven learning
methods, which aims to predict students' future performance given their past
question response sequences to trace their knowledge states. However, current
deep learning approaches only focus on enhancing prediction accuracy, but
neglecting the discrimination imbalance of responses. That is, a considerable
proportion of question responses are weak to discriminate students' knowledge
states, but equally considered compared to other discriminative responses, thus
hurting the ability of tracing students' personalized knowledge states. To
tackle this issue, we propose DR4KT for Knowledge Tracing, which reweights the
contribution of different responses according to their discrimination in
training. For retaining high prediction accuracy on low discriminative
responses after reweighting, DR4KT also introduces a discrimination-aware score
fusion technique to make a proper combination between student knowledge mastery
and the questions themselves. Comprehensive experimental results show that our
DR4KT applied on four mainstream KT methods significantly improves their
performance on three widely-used datasets. | [
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] |
Assessing the quality of movements for post-stroke patients during the
rehabilitation phase is vital given that there is no standard stroke
rehabilitation plan for all the patients. In fact, it depends basically on the
patient's functional independence and its progress along the rehabilitation
sessions. To tackle this challenge and make neurorehabilitation more agile, we
propose an automatic assessment pipeline that starts by recognizing patients'
movements by means of a shallow deep learning architecture, then measuring the
movement quality using jerk measure and related measures. A particularity of
this work is that the dataset used is clinically relevant, since it represents
movements inspired from Fugl-Meyer a well common upper-limb clinical stroke
assessment scale for stroke patients. We show that it is possible to detect the
contrast between healthy and patients movements in terms of smoothness, besides
achieving conclusions about the patients' progress during the rehabilitation
sessions that correspond to the clinicians' findings about each case. | [
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Enterprise resource planning (ERP) software brings resources, data together
to keep software-flow within business processes in a company. However, cloud
computing's cheap, easy and quick management promise pushes business-owners for
a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP
development involves a cyclic process, namely planning, implementing, testing
and upgrading, its adoption is realized as a deep recurrent neural network
problem. Eventually, a classification algorithm based on long short term memory
(LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption
features. Our theoretical model is validated over a reference model by
articulating key players, services, architecture, functionalities. Qualitative
survey is conducted among users by considering technology, innovation and
resistance issues, to formulate hypotheses on key adoption factors. | [
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] |
Bilevel optimization reveals the inner structure of otherwise oblique
optimization problems, such as hyperparameter tuning and meta-learning. A
common goal in bilevel optimization is to find stationary points of the
hyper-objective function. Although this hyper-objective approach is widely
used, its theoretical properties have not been thoroughly investigated in cases
where the lower-level functions lack strong convexity. In this work, we take a
step forward and study the hyper-objective approach without the typical
lower-level strong convexity assumption. Our hardness results show that the
hyper-objective of general convex lower-level functions can be intractable
either to evaluate or to optimize. To tackle this challenge, we introduce the
gradient dominant condition, which strictly relaxes the strong convexity
assumption by allowing the lower-level solution set to be non-singleton. Under
the gradient dominant condition, we propose the Inexact Gradient-Free Method
(IGFM), which uses the Switching Gradient Method (SGM) as the zeroth order
oracle, to find an approximate stationary point of the hyper-objective. We also
extend our results to nonsmooth lower-level functions under the weak sharp
minimum condition. | [
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Recommender systems (RS) commonly retrieve potential candidate items for
users from a massive number of items by modeling user interests based on
historical interactions. However, historical interaction data is highly sparse,
and most items are long-tail items, which limits the representation learning
for item discovery. This problem is further augmented by the discovery of novel
or cold-start items. For example, after a user displays interest in bitcoin
financial investment shows in the podcast space, a recommender system may want
to suggest, e.g., a newly released blockchain episode from a more technical
show. Episode correlations help the discovery, especially when interaction data
of episodes is limited. Accordingly, we build upon the classical Two-Tower
model and introduce the novel Multi-Source Augmentations using a Contrastive
Learning framework (MSACL) to enhance episode embedding learning by
incorporating positive episodes from numerous correlated semantics. Extensive
experiments on a real-world podcast recommendation dataset from a large audio
streaming platform demonstrate the effectiveness of the proposed framework for
user podcast exploration and cold-start episode recommendation. | [
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] |
We propose to integrate weapon system features (such as weapon system
manufacturer, deployment time and location, storage time and location, etc.)
into a parameterized Cox-Weibull [1] reliability model via a neural network,
like DeepSurv [2], to improve predictive maintenance. In parallel, we develop
an alternative Bayesian model by parameterizing the Weibull parameters with a
neural network and employing dropout methods such as Monte-Carlo (MC)-dropout
for comparative purposes. Due to data collection procedures in weapon system
testing we employ a novel interval-censored log-likelihood which incorporates
Monte-Carlo Markov Chain (MCMC) [3] sampling of the Weibull parameters during
gradient descent optimization. We compare classification metrics such as
receiver operator curve (ROC) area under the curve (AUC), precision-recall (PR)
AUC, and F scores to show our model generally outperforms traditional powerful
models such as XGBoost and the current standard conditional Weibull probability
density estimation model. | [
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] |
Developing agents that can execute multiple skills by learning from
pre-collected datasets is an important problem in robotics, where online
interaction with the environment is extremely time-consuming. Moreover,
manually designing reward functions for every single desired skill is
prohibitive. Prior works targeted these challenges by learning goal-conditioned
policies from offline datasets without manually specified rewards, through
hindsight relabelling. These methods suffer from the issue of sparsity of
rewards, and fail at long-horizon tasks. In this work, we propose a novel
self-supervised learning phase on the pre-collected dataset to understand the
structure and the dynamics of the model, and shape a dense reward function for
learning policies offline. We evaluate our method on three continuous control
tasks, and show that our model significantly outperforms existing approaches,
especially on tasks that involve long-term planning. | [
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Through the generalization of deep learning, the research community has
addressed critical challenges in the network security domain, like malware
identification and anomaly detection. However, they have yet to discuss
deploying them on Internet of Things (IoT) devices for day-to-day operations.
IoT devices are often limited in memory and processing power, rendering the
compute-intensive deep learning environment unusable. This research proposes a
way to overcome this barrier by bypassing feature engineering in the deep
learning pipeline and using raw packet data as input. We introduce a feature
engineering-less machine learning (ML) process to perform malware detection on
IoT devices. Our proposed model, "Feature engineering-less-ML (FEL-ML)," is a
lighter-weight detection algorithm that expends no extra computations on
"engineered" features. It effectively accelerates the low-powered IoT edge. It
is trained on unprocessed byte-streams of packets. Aside from providing better
results, it is quicker than traditional feature-based methods. FEL-ML
facilitates resource-sensitive network traffic security with the added benefit
of eliminating the significant investment by subject matter experts in feature
engineering. | [
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] |
General intelligence requires solving tasks across many domains. Current
reinforcement learning algorithms carry this potential but are held back by the
resources and knowledge required to tune them for new tasks. We present
DreamerV3, a general and scalable algorithm based on world models that
outperforms previous approaches across a wide range of domains with fixed
hyperparameters. These domains include continuous and discrete actions, visual
and low-dimensional inputs, 2D and 3D worlds, different data budgets, reward
frequencies, and reward scales. We observe favorable scaling properties of
DreamerV3, with larger models directly translating to higher data-efficiency
and final performance. Applied out of the box, DreamerV3 is the first algorithm
to collect diamonds in Minecraft from scratch without human data or curricula,
a long-standing challenge in artificial intelligence. Our general algorithm
makes reinforcement learning broadly applicable and allows scaling to hard
decision-making problems. | [
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] |
Identifying whether a given sample is an outlier or not is an important issue
in various real-world domains. This study aims to solve the unsupervised
outlier detection problem where training data contain outliers, but any label
information about inliers and outliers is not given. We propose a powerful and
efficient learning framework to identify outliers in a training data set using
deep neural networks. We start with a new observation called the
inlier-memorization (IM) effect. When we train a deep generative model with
data contaminated with outliers, the model first memorizes inliers before
outliers. Exploiting this finding, we develop a new method called the outlier
detection via the IM effect (ODIM). The ODIM only requires a few updates; thus,
it is computationally efficient, tens of times faster than other
deep-learning-based algorithms. Also, the ODIM filters out outliers
successfully, regardless of the types of data, such as tabular, image, and
sequential. We empirically demonstrate the superiority and efficiency of the
ODIM by analyzing 20 data sets. | [
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] |
Recently, there are many trials to apply reinforcement learning in asset
allocation for earning more stable profits. In this paper, we compare
performance between several reinforcement learning algorithms - actor-only,
actor-critic and PPO models. Furthermore, we analyze each models' character and
then introduce the advanced algorithm, so called Reward clipping model. It
seems that the Reward Clipping model is better than other existing models in
finance domain, especially portfolio optimization - it has strength both in
bull and bear markets. Finally, we compare the performance for these models
with traditional investment strategies during decreasing and increasing
markets. | [
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] |
For service robots to become general-purpose in everyday household
environments, they need not only a large library of primitive skills, but also
the ability to quickly learn novel tasks specified by users. Fine-tuning neural
networks on a variety of downstream tasks has been successful in many vision
and language domains, but research is still limited on transfer learning
between diverse long-horizon tasks. We propose that, compared to reinforcement
learning for a new household activity from scratch, home robots can benefit
from transferring the value and policy networks trained for similar tasks. We
evaluate this idea in the BEHAVIOR simulation benchmark which includes a large
number of household activities and a set of action primitives. For easy mapping
between state spaces of different tasks, we provide a text-based representation
and leverage language models to produce a common embedding space. The results
show that the selection of similar source activities can be informed by the
semantic similarity of state and goal descriptions with the target task. We
further analyze the results and discuss ways to overcome the problem of
catastrophic forgetting. | [
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] |
As automated decision making and decision assistance systems become common in
everyday life, research on the prevention or mitigation of potential harms that
arise from decisions made by these systems has proliferated. However, various
research communities have independently conceptualized these harms, envisioned
potential applications, and proposed interventions. The result is a somewhat
fractured landscape of literature focused generally on ensuring decision-making
algorithms "do the right thing". In this paper, we compare and discuss work
across two major subsets of this literature: algorithmic fairness, which
focuses primarily on predictive systems, and ethical decision making, which
focuses primarily on sequential decision making and planning. We explore how
each of these settings has articulated its normative concerns, the viability of
different techniques for these different settings, and how ideas from each
setting may have utility for the other. | [
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] |
In AI-assisted decision-making, it is critical for human decision-makers to
know when to trust AI and when to trust themselves. However, prior studies
calibrated human trust only based on AI confidence indicating AI's correctness
likelihood (CL) but ignored humans' CL, hindering optimal team decision-making.
To mitigate this gap, we proposed to promote humans' appropriate trust based on
the CL of both sides at a task-instance level. We first modeled humans' CL by
approximating their decision-making models and computing their potential
performance in similar instances. We demonstrated the feasibility and
effectiveness of our model via two preliminary studies. Then, we proposed three
CL exploitation strategies to calibrate users' trust explicitly/implicitly in
the AI-assisted decision-making process. Results from a between-subjects
experiment (N=293) showed that our CL exploitation strategies promoted more
appropriate human trust in AI, compared with only using AI confidence. We
further provided practical implications for more human-compatible AI-assisted
decision-making. | [
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] |
Industrial robots are widely used in various manufacturing environments due
to their efficiency in doing repetitive tasks such as assembly or welding. A
common problem for these applications is to reach a destination without
colliding with obstacles or other robot arms. Commonly used sampling-based path
planning approaches such as RRT require long computation times, especially in
complex environments. Furthermore, the environment in which they are employed
needs to be known beforehand. When utilizing the approaches in new
environments, a tedious engineering effort in setting hyperparameters needs to
be conducted, which is time- and cost-intensive. On the other hand, Deep
Reinforcement Learning has shown remarkable results in dealing with unknown
environments, generalizing new problem instances, and solving motion planning
problems efficiently. On that account, this paper proposes a
Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We
evaluated our model against state-of-the-art sampling-based planners in several
experiments. The results show the superiority of our planner in terms of path
length and execution time. | [
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] |
ChatGPT is not only fun to chat with, but it also searches information,
answers questions, and gives advice. With consistent moral advice, it might
improve the moral judgment and decisions of users, who often hold contradictory
moral beliefs. Unfortunately, ChatGPT turns out highly inconsistent as a moral
advisor. Nonetheless, it influences users' moral judgment, we find in an
experiment, even if they know they are advised by a chatting bot, and they
underestimate how much they are influenced. Thus, ChatGPT threatens to corrupt
rather than improves users' judgment. These findings raise the question of how
to ensure the responsible use of ChatGPT and similar AI. Transparency is often
touted but seems ineffective. We propose training to improve digital literacy. | [
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] |
In this article, we propose the Artificial Intelligence Security Taxonomy to
systematize the knowledge of threats, vulnerabilities, and security controls of
machine-learning-based (ML-based) systems. We first classify the damage caused
by attacks against ML-based systems, define ML-specific security, and discuss
its characteristics. Next, we enumerate all relevant assets and stakeholders
and provide a general taxonomy for ML-specific threats. Then, we collect a wide
range of security controls against ML-specific threats through an extensive
review of recent literature. Finally, we classify the vulnerabilities and
controls of an ML-based system in terms of each vulnerable asset in the
system's entire lifecycle. | [
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] |
Integrating human knowledge into neural networks has the potential to improve
their robustness and interpretability. We have developed a novel approach to
integrate knowledge from ontologies into the structure of a Transformer network
which we call ontology pre-training: we train the network to predict membership
in ontology classes as a way to embed the structure of the ontology into the
network, and subsequently fine-tune the network for the particular prediction
task. We apply this approach to a case study in predicting the potential
toxicity of a small molecule based on its molecular structure, a challenging
task for machine learning in life sciences chemistry. Our approach improves on
the state of the art, and moreover has several additional benefits. First, we
are able to show that the model learns to focus attention on more meaningful
chemical groups when making predictions with ontology pre-training than
without, paving a path towards greater robustness and interpretability. Second,
the training time is reduced after ontology pre-training, indicating that the
model is better placed to learn what matters for toxicity prediction with the
ontology pre-training than without. This strategy has general applicability as
a neuro-symbolic approach to embed meaningful semantics into neural networks. | [
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] |
Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms
primarily used in Bayesian statistics to sample from a target distribution when
direct sampling is challenging. Existing work on Bayesian decision trees uses
MCMC. Unfortunately, this can be slow, especially when considering large
volumes of data. It is hard to parallelise the accept-reject component of the
MCMC. None-the-less, we propose two methods for exploiting parallelism in the
MCMC: in the first, we replace the MCMC with another numerical Bayesian
approach, the Sequential Monte Carlo (SMC) sampler, which has the appealing
property that it is an inherently parallel algorithm; in the second, we
consider data partitioning. Both methods use multi-core processing with a
HighPerformance Computing (HPC) resource. We test the two methods in various
study settings to determine which method is the most beneficial for each test
case. Experiments show that data partitioning has limited utility in the
settings we consider and that the use of the SMC sampler can improve run-time
(compared to the sequential implementation) by up to a factor of 343. | [
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] |
Transformer models gain popularity because of their superior inference
accuracy and inference throughput. However, the transformer is
computation-intensive, causing a long inference time. The existing works on
transformer inference acceleration have limitations caused by either the
modification of transformer architectures or the need of specialized hardware.
In this paper, we identify the opportunities of using memoization to accelerate
the self-attention mechanism in transformers without the above limitations.
Built upon a unique observation that there is rich similarity in attention
computation across inference sequences, we build a memoization database that
leverages the emerging big memory system. We introduce a novel embedding
technique to find semantically similar inputs to identify computation
similarity. We also introduce a series of techniques such as memory mapping and
selective memoization to avoid memory copy and unnecessary overhead. We enable
22% inference-latency reduction on average (up to 68%) with negligible loss in
inference accuracy. | [
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] |
In distributed multiple-input multiple-output (D-MIMO) networks, power
control is crucial to optimize the spectral efficiencies of users and max-min
fairness (MMF) power control is a commonly used strategy as it satisfies
uniform quality-of-service to all users. The optimal solution of MMF power
control requires high complexity operations and hence deep neural network based
artificial intelligence (AI) solutions are proposed to decrease the complexity.
Although quite accurate models can be achieved by using AI, these models have
some intrinsic vulnerabilities against adversarial attacks where carefully
crafted perturbations are applied to the input of the AI model. In this work,
we show that threats against the target AI model which might be originated from
malicious users or radio units can substantially decrease the network
performance by applying a successful adversarial sample, even in the most
constrained circumstances. We also demonstrate that the risk associated with
these kinds of adversarial attacks is higher than the conventional attack
threats. Detailed simulations reveal the effectiveness of adversarial attacks
and the necessity of smart defense techniques. | [
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] |
Artificial intelligence (AI) technologies are widely deployed in smartphone
photography; and prompt-based image synthesis models have rapidly become
commonplace. In this paper, we describe a Research-through-Design (RtD) project
which explores this shift in the means and modes of image production via the
creation and use of the Entoptic Field Camera. Entoptic phenomena usually refer
to perceptions of floaters or bright blue dots stemming from the physiological
interplay of the eye and brain. We use the term entoptic as a metaphor to
investigate how the material interplay of data and models in AI technologies
shapes human experiences of reality. Through our case study using first-person
design and a field study, we offer implications for critical, reflective,
more-than-human and ludic design to engage AI technologies; the
conceptualisation of an RtD research space which contributes to AI literacy
discourses; and outline a research trajectory concerning materiality and design
affordances of AI technologies. | [
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] |
Neural networks (NNs) have various applications in AI, but explaining their
decisions remains challenging. Existing approaches often focus on explaining
how changing individual inputs affects NNs' outputs. However, an explanation
that is consistent with the input-output behaviour of an NN is not necessarily
faithful to the actual mechanics thereof. In this paper, we exploit
relationships between multi-layer perceptrons (MLPs) and quantitative
argumentation frameworks (QAFs) to create argumentative explanations for the
mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining
as much of the original structure as possible. It then translates the sparse
MLP into an equivalent QAF to shed light on the underlying decision process of
the MLP, producing global and/or local explanations. We demonstrate
experimentally that SpArX can give more faithful explanations than existing
approaches, while simultaneously providing deeper insights into the actual
reasoning process of MLPs. | [
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] |
The world around us has undergone a radical transformation due to rapid
technological advancement in recent decades. The industry of the future
generation is evolving, and artificial intelligence is the following change in
the making popularly known as Industry 4.0. Indeed, experts predict that
artificial intelligence(AI) will be the main force behind the following
significant virtual shift in the way we stay, converse, study, live,
communicate and conduct business. All facets of our social connection are being
transformed by this growing technology. One of the newest areas of educational
technology is Artificial Intelligence in the field of Education(AIEd).This
study emphasizes the different applications of artificial intelligence in
education from both an industrial and academic standpoint. It highlights the
most recent contextualized learning novel transformative evaluations and
advancements in sophisticated tutoring systems. It analyses the AIEd's ethical
component and the influence of the transition on people, particularly students
and instructors as well. Finally, this article touches on AIEd's potential
future research and practices. The goal of this study is to introduce the
present-day applications to its intended audience. | [
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Tracking climbers' activity to improve services and make the best use of
their infrastructure is a concern for climbing gyms. Each climbing session must
be analyzed from beginning till lowering of the climber. Therefore, spotting
the climbers descending is crucial since it indicates when the ascent has come
to an end. This problem must be addressed while preserving privacy and
convenience of the climbers and the costs of the gyms. To this aim, a hardware
prototype is developed to collect data using accelerometer sensors attached to
a piece of climbing equipment mounted on the wall, called quickdraw, that
connects the climbing rope to the bolt anchors. The corresponding sensors are
configured to be energy-efficient, hence become practical in terms of expenses
and time consumption for replacement when using in large quantity in a climbing
gym. This paper describes hardware specifications, studies data measured by the
sensors in ultra-low power mode, detect sensors' orientation patterns during
lowering different routes, and develop an supervised approach to identify
lowering. | [
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] |
Lack of diversity in data collection has caused significant failures in
machine learning (ML) applications. While ML developers perform post-collection
interventions, these are time intensive and rarely comprehensive. Thus, new
methods to track & manage data collection, iteration, and model training are
necessary for evaluating whether datasets reflect real world variability. We
present designing data, an iterative approach to data collection connecting HCI
concepts with ML techniques. Our process includes (1) Pre-Collection Planning,
to reflexively prompt and document expected data distributions; (2) Collection
Monitoring, to systematically encourage sampling diversity; and (3) Data
Familiarity, to identify samples that are unfamiliar to a model using density
estimation. We apply designing data to a data collection and modeling task. We
find models trained on ''designed'' datasets generalize better across
intersectional groups than those trained on similarly sized but less targeted
datasets, and that data familiarity is effective for debugging datasets. | [
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] |
Diffusion models have emerged as powerful generative models in the
text-to-image domain. This paper studies their application as
observation-to-action models for imitating human behaviour in sequential
environments. Human behaviour is stochastic and multimodal, with structured
correlations between action dimensions. Meanwhile, standard modelling choices
in behaviour cloning are limited in their expressiveness and may introduce bias
into the cloned policy. We begin by pointing out the limitations of these
choices. We then propose that diffusion models are an excellent fit for
imitating human behaviour, since they learn an expressive distribution over the
joint action space. We introduce several innovations to make diffusion models
suitable for sequential environments; designing suitable architectures,
investigating the role of guidance, and developing reliable sampling
strategies. Experimentally, diffusion models closely match human demonstrations
in a simulated robotic control task and a modern 3D gaming environment. | [
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] |
We consider stochastic unconstrained bilevel optimization problems when only
the first-order gradient oracles are available. While numerous optimization
methods have been proposed for tackling bilevel problems, existing methods
either tend to require possibly expensive calculations regarding Hessians of
lower-level objectives, or lack rigorous finite-time performance guarantees. In
this work, we propose a Fully First-order Stochastic Approximation (F2SA)
method, and study its non-asymptotic convergence properties. Specifically, we
show that F2SA converges to an $\epsilon$-stationary solution of the bilevel
problem after $\epsilon^{-7/2}, \epsilon^{-5/2}$, and $\epsilon^{-3/2}$
iterations (each iteration using $O(1)$ samples) when stochastic noises are in
both level objectives, only in the upper-level objective, and not present
(deterministic settings), respectively. We further show that if we employ
momentum-assisted gradient estimators, the iteration complexities can be
improved to $\epsilon^{-5/2}, \epsilon^{-4/2}$, and $\epsilon^{-3/2}$,
respectively. We demonstrate even superior practical performance of the
proposed method over existing second-order based approaches on MNIST
data-hypercleaning experiments. | [
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] |
Description logic (DL) ontologies extend knowledge graphs (KGs) with
conceptual information and logical background knowledge. In recent years, there
has been growing interest in inductive reasoning techniques for such
ontologies, which promise to complement classical deductive reasoning
algorithms. Similar to KG completion, several existing approaches learn
ontology embeddings in a latent space, while additionally ensuring that they
faithfully capture the logical semantics of the underlying DL. However, they
suffer from several shortcomings, mainly due to a limiting role representation.
We propose Box$^2$EL, which represents both concepts and roles as boxes (i.e.,
axis-aligned hyperrectangles) and demonstrate how it overcomes the limitations
of previous methods. We theoretically prove the soundness of our model and
conduct an extensive experimental evaluation, achieving state-of-the-art
results across a variety of datasets. As part of our evaluation, we introduce a
novel benchmark for subsumption prediction involving both atomic and complex
concepts. | [
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] |
As a framework for sequential decision-making, Reinforcement Learning (RL)
has been regarded as an essential component leading to Artificial General
Intelligence (AGI). However, RL is often criticized for having the same
training environment as the test one, which also hinders its application in the
real world. To mitigate this problem, Distributionally Robust RL (DRRL) is
proposed to improve the worst performance in a set of environments that may
contain the unknown test environment. Due to the nonlinearity of the robustness
goal, most of the previous work resort to the model-based approach, learning
with either an empirical distribution learned from the data or a simulator that
can be sampled infinitely, which limits their applications in simple dynamics
environments. In contrast, we attempt to design a DRRL algorithm that can be
trained along a single trajectory, i.e., no repeated sampling from a state.
Based on the standard Q-learning, we propose distributionally robust Q-learning
with the single trajectory (DRQ) and its average-reward variant named
differential DRQ. We provide asymptotic convergence guarantees and experiments
for both settings, demonstrating their superiority in the perturbed
environments against the non-robust ones. | [
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] |
Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class
(MEC) is an important primitive in causal analysis. The central resource from
the perspective of computational complexity is the delay, that is, the time an
algorithm that lists all members of the class requires between two consecutive
outputs. Commonly used algorithms for this task utilize the rules proposed by
Meek (1995) or the transformational characterization by Chickering (1995), both
resulting in superlinear delay. In this paper, we present the first linear-time
delay algorithm. On the theoretical side, we show that our algorithm can be
generalized to enumerate DAGs represented by models that incorporate background
knowledge, such as MPDAGs; on the practical side, we provide an efficient
implementation and evaluate it in a series of experiments. Complementary to the
linear-time delay algorithm, we also provide intriguing insights into Markov
equivalence itself: All members of an MEC can be enumerated such that two
successive DAGs have structural Hamming distance at most three. | [
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] |
Domain-specific machine learning (ML) accelerators such as Google's TPU and
Apple's Neural Engine now dominate CPUs and GPUs for energy-efficient ML
processing. However, the evolution of electronic accelerators is facing
fundamental limits due to the limited computation density of monolithic
processing chips and the reliance on slow metallic interconnects. In this
paper, we present a vision of how optical computation and communication can be
integrated into 2.5D chiplet platforms to drive an entirely new class of
sustainable and scalable ML hardware accelerators. We describe how cross-layer
design and fabrication of optical devices, circuits, and architectures, and
hardware/software codesign can help design efficient photonics-based 2.5D
chiplet platforms to accelerate emerging ML workloads. | [
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] |
So-called implicit behavioral cloning with energy-based models has shown
promising results in robotic manipulation tasks. We tested if the method's
advantages carry on to controlling the steering of a real self-driving car with
an end-to-end driving model. We performed an extensive comparison of the
implicit behavioral cloning approach with explicit baseline approaches, all
sharing the same neural network backbone architecture. Baseline explicit models
were trained with regression (MAE) loss, classification loss (softmax and
cross-entropy on a discretization), or as mixture density networks (MDN). While
models using the energy-based formulation performed comparably to baseline
approaches in terms of safety driver interventions, they had a higher whiteness
measure, indicating higher jerk. To alleviate this, we show two methods that
can be used to improve the smoothness of steering. We confirmed that
energy-based models handle multimodalities slightly better than simple
regression, but this did not translate to significantly better driving ability.
We argue that the steering-only road-following task has too few multimodalities
to benefit from energy-based models. This shows that applying implicit
behavioral cloning to real-world tasks can be challenging, and further
investigation is needed to bring out the theoretical advantages of energy-based
models. | [
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] |
As a prevalent problem in online advertising, CTR prediction has attracted
plentiful attention from both academia and industry. Recent studies have been
reported to establish CTR prediction models in the graph neural networks (GNNs)
framework. However, most of GNNs-based models handle feature interactions in a
complete graph, while ignoring causal relationships among features, which
results in a huge drop in the performance on out-of-distribution data. This
paper is dedicated to developing a causality-based CTR prediction model in the
GNNs framework (Causal-GNN) integrating representations of feature graph, user
graph and ad graph in the context of online advertising. In our model, a
structured representation learning method (GraphFwFM) is designed to capture
high-order representations on feature graph based on causal discovery among
field features in gated graph neural networks (GGNNs), and GraphSAGE is
employed to obtain graph representations of users and ads. Experiments
conducted on three public datasets demonstrate the superiority of Causal-GNN in
AUC and Logloss and the effectiveness of GraphFwFM in capturing high-order
representations on causal feature graph. | [
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] |
We propose a supervised deep learning (DL) approach to perform adaptive
zoning on time dependent partial differential equations that model the
propagation of 1D shock waves in a compressible medium. We train a neural
network on a dataset composed of different static shock profiles associated
with the corresponding adapted meshes computed with standard adaptive zoning
techniques. We show that the trained DL model learns how to capture the
presence of shocks in the domain and generates at each time step an adapted
non-uniform mesh that relocates the grid nodes to improve the accuracy of
Lax-Wendroff and fifth order weighted essentially non-oscillatory (WENO5) space
discretization schemes. We also show that the surrogate DL model reduces the
computational time to perform adaptive zoning by at least a 2x factor with
respect to standard techniques without compromising the accuracy of the
reconstruction of the physical quantities of interest. | [
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] |
We present a data-driven modeling and control framework for physics-based
building emulators. Our approach comprises: (a) Offline training of
differentiable surrogate models that speed up model evaluations, provide cheap
gradients, and have good predictive accuracy for the receding horizon in Model
Predictive Control (MPC) and (b) Formulating and solving nonlinear building
HVAC MPC problems. We extensively verify the modeling and control performance
using multiple surrogate models and optimization frameworks for different
available test cases in the Building Optimization Testing Framework (BOPTEST).
The framework is compatible with other modeling techniques and customizable
with different control formulations. The modularity makes the approach
future-proof for test cases currently in development for physics-based building
emulators and provides a path toward prototyping predictive controllers in
large buildings. | [
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] |
Variational quantum algorithms represent a promising approach to quantum
machine learning where classical neural networks are replaced by parametrized
quantum circuits. However, both approaches suffer from a clear limitation, that
is a lack of interpretability. Here, we present a variational method to
quantize projective simulation (PS), a reinforcement learning model aimed at
interpretable artificial intelligence. Decision making in PS is modeled as a
random walk on a graph describing the agent's memory. To implement the
quantized model, we consider quantum walks of single photons in a lattice of
tunable Mach-Zehnder interferometers trained via variational algorithms. Using
an example from transfer learning, we show that the quantized PS model can
exploit quantum interference to acquire capabilities beyond those of its
classical counterpart. Finally, we discuss the role of quantum interference for
training and tracing the decision making process, paving the way for
realizations of interpretable quantum learning agents. | [
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] |
Black box optimisation of an unknown function from expensive and noisy
evaluations is a ubiquitous problem in machine learning, academic research and
industrial production. An abstraction of the problem can be formulated as a
kernel based bandit problem (also known as Bayesian optimisation), where a
learner aims at optimising a kernelized function through sequential noisy
observations. The existing work predominantly assumes feedback is immediately
available; an assumption which fails in many real world situations, including
recommendation systems, clinical trials and hyperparameter tuning. We consider
a kernel bandit problem under stochastically delayed feedback, and propose an
algorithm with $\tilde{\mathcal{O}}(\sqrt{\Gamma_k(T)T}+\mathbb{E}[\tau])$
regret, where $T$ is the number of time steps, $\Gamma_k(T)$ is the maximum
information gain of the kernel with $T$ observations, and $\tau$ is the delay
random variable. This represents a significant improvement over the state of
the art regret bound of
$\tilde{\mathcal{O}}(\Gamma_k(T)\sqrt{T}+\mathbb{E}[\tau]\Gamma_k(T))$ reported
in Verma et al. (2022). In particular, for very non-smooth kernels, the
information gain grows almost linearly in time, trivializing the existing
results. We also validate our theoretical results with simulations. | [
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] |
Designing molecular structures with desired chemical properties is an
essential task in drug discovery and material design. However, finding
molecules with the optimized desired properties is still a challenging task due
to combinatorial explosion of candidate space of molecules. Here we propose a
novel \emph{decomposition-and-reassembling} based approach, which does not
include any optimization in hidden space and our generation process is highly
interpretable. Our method is a two-step procedure: In the first decomposition
step, we apply frequent subgraph mining to a molecular database to collect
smaller size of subgraphs as building blocks of molecules. In the second
reassembling step, we search desirable building blocks guided via reinforcement
learning and combine them to generate new molecules. Our experiments show that
not only can our method find better molecules in terms of two standard
criteria, the penalized $\log P$ and drug-likeness, but also generate drug
molecules with showing the valid intermediate molecules. | [
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] |
We introduce EPIC-SOUNDS, a large-scale dataset of audio annotations
capturing temporal extents and class labels within the audio stream of the
egocentric videos. We propose an annotation pipeline where annotators
temporally label distinguishable audio segments and describe the action that
could have caused this sound. We identify actions that can be discriminated
purely from audio, through grouping these free-form descriptions of audio into
classes. For actions that involve objects colliding, we collect human
annotations of the materials of these objects (e.g. a glass object being placed
on a wooden surface), which we verify from visual labels, discarding
ambiguities. Overall, EPIC-SOUNDS includes 78.4k categorised segments of
audible events and actions, distributed across 44 classes as well as 39.2k
non-categorised segments. We train and evaluate two state-of-the-art audio
recognition models on our dataset, highlighting the importance of audio-only
labels and the limitations of current models to recognise actions that sound. | [
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] |
Enabling resilient autonomous motion planning requires robust predictions of
surrounding road users' future behavior. In response to this need and the
associated challenges, we introduce our model titled MTP-GO. The model encodes
the scene using temporal graph neural networks to produce the inputs to an
underlying motion model. The motion model is implemented using neural ordinary
differential equations where the state-transition functions are learned with
the rest of the model. Multimodal probabilistic predictions are obtained by
combining the concept of mixture density networks and Kalman filtering. The
results illustrate the predictive capabilities of the proposed model across
various data sets, outperforming several state-of-the-art methods on a number
of metrics. | [
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Molecular dynamic simulations are important in computational physics,
chemistry, material, and biology. Machine learning-based methods have shown
strong abilities in predicting molecular energy and properties and are much
faster than DFT calculations. Molecular energy is at least related to atoms,
bonds, bond angles, torsion angles, and nonbonding atom pairs. Previous
Transformer models only use atoms as inputs which lack explicit modeling of the
aforementioned factors. To alleviate this limitation, we propose Moleformer, a
novel Transformer architecture that takes nodes (atoms) and edges (bonds and
nonbonding atom pairs) as inputs and models the interactions among them using
rotational and translational invariant geometry-aware spatial encoding.
Proposed spatial encoding calculates relative position information including
distances and angles among nodes and edges. We benchmark Moleformer on OC20 and
QM9 datasets, and our model achieves state-of-the-art on the initial state to
relaxed energy prediction of OC20 and is very competitive in QM9 on predicting
quantum chemical properties compared to other Transformer and Graph Neural
Network (GNN) methods which proves the effectiveness of the proposed
geometry-aware spatial encoding in Moleformer. | [
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] |
Future generations of mobile networks are expected to contain more and more
antennas with growing complexity and more parameters. Optimizing these
parameters is necessary for ensuring the good performance of the network. The
scale of mobile networks makes it challenging to optimize antenna parameters
using manual intervention or hand-engineered strategies. Reinforcement learning
is a promising technique to address this challenge but existing methods often
use local optimizations to scale to large network deployments. We propose a new
multi-agent reinforcement learning algorithm to optimize mobile network
configurations globally. By using a value decomposition approach, our algorithm
can be trained from a global reward function instead of relying on an ad-hoc
decomposition of the network performance across the different cells. The
algorithm uses a graph neural network architecture which generalizes to
different network topologies and learns coordination behaviors. We empirically
demonstrate the performance of the algorithm on an antenna tilt tuning problem
and a joint tilt and power control problem in a simulated environment. | [
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] |
Many visualizations have been developed for explainable AI (XAI), but they
often require further reasoning by users to interpret. We argue that XAI should
support diagrammatic and abductive reasoning for the AI to perform hypothesis
generation and evaluation to reduce the interpretability gap. We propose
Diagrammatization to i) perform Peircean abductive-deductive reasoning, ii)
follow domain conventions, and iii) explain with diagrams visually or verbally.
We implemented DiagramNet for a clinical application to predict cardiac
diagnoses from heart auscultation, and explain with shape-based murmur
diagrams. In modeling studies, we found that DiagramNet not only provides
faithful murmur shape explanations, but also has better prediction performance
than baseline models. We further demonstrate the interpretability and
trustworthiness of diagrammatic explanations in a qualitative user study with
medical students, showing that clinically-relevant, diagrammatic explanations
are preferred over technical saliency map explanations. This work contributes
insights into providing domain-conventional abductive explanations for
user-centric XAI. | [
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Deep artificial neural networks show high predictive performance in many
fields, but they do not afford statistical inferences and their black-box
operations are too complicated for humans to comprehend. Because positing that
a relationship exists is often more important than prediction in scientific
experiments and research models, machine learning is far less frequently used
than inferential statistics. Additionally, statistics calls for improving the
test of theory by showing the magnitude of the phenomena being studied. This
article extends current XAI methods and develops a model agnostic hypothesis
testing framework for machine learning. First, Fisher's variable permutation
algorithm is tweaked to compute an effect size measure equivalent to Cohen's f2
for OLS regression models. Second, the Mann-Kendall test of monotonicity and
the Theil-Sen estimator is applied to Apley's accumulated local effect plots to
specify a variable's direction of influence and statistical significance. The
usefulness of this approach is demonstrated on an artificial data set and a
social survey with a Python sandbox implementation. | [
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] |
We introduce a novel approach to user authentication called Motion ID. The
method employs motion sensing provided by inertial measurement units (IMUs),
using it to verify the persons identity via short time series of IMU data
captured by the mobile device. The paper presents two labeled datasets with
unlock events: the first features IMU measurements, provided by six users who
continuously collected data on six different smartphones for a period of 12
weeks. The second one contains 50 hours of IMU data for one specific motion
pattern, provided by 101 users. Moreover, we present a two-stage user
authentication process that employs motion pattern identification and user
verification and is based on data preprocessing and machine learning. The
Results section details the assessment of the method proposed, comparing it
with existing biometric authentication methods and the Android biometric
standard. The method has demonstrated high accuracy, indicating that it could
be successfully used in combination with existing methods. Furthermore, the
method exhibits significant promise as a standalone solution. We provide the
datasets to the scholarly community and share our project code. | [
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] |
Posit has been a promising alternative to the IEEE-754 floating point format
for deep learning applications due to its better trade-off between dynamic
range and accuracy. However, hardware implementation of posit arithmetic
requires further exploration, especially for the dot-product operations
dominated in deep neural networks (DNNs). It has been implemented by either the
combination of multipliers and an adder tree or cascaded fused multiply-add
units, leading to poor computational efficiency and excessive hardware
overhead. To address this issue, we propose an open-source posit dot-product
unit, namely PDPU, that facilitates resource-efficient and high-throughput
dot-product hardware implementation. PDPU not only features the fused and
mixed-precision architecture that eliminates redundant latency and hardware
resources, but also has a fine-grained 6-stage pipeline, improving
computational efficiency. A configurable PDPU generator is further developed to
meet the diverse needs of various DNNs for computational accuracy. Experimental
results evaluated under the 28nm CMOS process show that PDPU reduces area,
latency, and power by up to 43%, 64%, and 70%, respectively, compared to the
existing implementations. Hence, PDPU has great potential as the computing core
of posit-based accelerators for deep learning applications. | [
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] |
To act in the world, robots rely on a representation of salient task aspects:
for example, to carry a cup of coffee, a robot must consider movement
efficiency and cup orientation in its behaviour. However, if we want robots to
act for and with people, their representations must not be just functional but
also reflective of what humans care about, i.e. their representations must be
aligned with humans'. In this survey, we pose that current reward and imitation
learning approaches suffer from representation misalignment, where the robot's
learned representation does not capture the human's representation. We suggest
that because humans will be the ultimate evaluator of robot performance in the
world, it is critical that we explicitly focus our efforts on aligning learned
task representations with humans, in addition to learning the downstream task.
We advocate that current representation learning approaches in robotics should
be studied from the perspective of how well they accomplish the objective of
representation alignment. To do so, we mathematically define the problem,
identify its key desiderata, and situate current robot learning methods within
this formalism. We conclude the survey by suggesting future directions for
exploring open challenges. | [
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] |
We consider the task of evaluating policies of algorithmic resource
allocation through randomized controlled trials (RCTs). Such policies are
tasked with optimizing the utilization of limited intervention resources, with
the goal of maximizing the benefits derived. Evaluation of such allocation
policies through RCTs proves difficult, notwithstanding the scale of the trial,
because the individuals' outcomes are inextricably interlinked through resource
constraints controlling the policy decisions. Our key contribution is to
present a new estimator leveraging our proposed novel concept, that involves
retrospective reshuffling of participants across experimental arms at the end
of an RCT. We identify conditions under which such reassignments are
permissible and can be leveraged to construct counterfactual trials, whose
outcomes can be accurately ascertained, for free. We prove theoretically that
such an estimator is more accurate than common estimators based on sample means
-- we show that it returns an unbiased estimate and simultaneously reduces
variance. We demonstrate the value of our approach through empirical
experiments on synthetic, semi-synthetic as well as real case study data and
show improved estimation accuracy across the board. | [
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] |
In recent years, mobile robot navigation approaches have become increasingly
important due to various application areas ranging from healthcare to warehouse
logistics. In particular, Deep Reinforcement Learning approaches have gained
popularity for robot navigation but are not easily accessible to non-experts
and complex to develop. In recent years, efforts have been made to make these
sophisticated approaches accessible to a wider audience. In this paper, we
present Arena-Web, a web-based development and evaluation suite for developing,
training, and testing DRL-based navigation planners for various robotic
platforms and scenarios. The interface is designed to be intuitive and engaging
to appeal to non-experts and make the technology accessible to a wider
audience. With Arena-Web and its interface, training and developing Deep
Reinforcement Learning agents is simplified and made easy without a single line
of code. The web-app is free to use and openly available under the link stated
in the supplementary materials. | [
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] |
Spatially resolved transcriptomics brings exciting breakthroughs to
single-cell analysis by providing physical locations along with gene
expression. However, as a cost of the extremely high spatial resolution, the
cellular level spatial transcriptomic data suffer significantly from missing
values. While a standard solution is to perform imputation on the missing
values, most existing methods either overlook spatial information or only
incorporate localized spatial context without the ability to capture long-range
spatial information. Using multi-head self-attention mechanisms and positional
encoding, transformer models can readily grasp the relationship between tokens
and encode location information. In this paper, by treating single cells as
spatial tokens, we study how to leverage transformers to facilitate spatial
tanscriptomics imputation. In particular, investigate the following two key
questions: (1) $\textit{how to encode spatial information of cells in
transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic
imputation}$. By answering these two questions, we present a transformer-based
imputation framework, SpaFormer, for cellular-level spatial transcriptomic
data. Extensive experiments demonstrate that SpaFormer outperforms existing
state-of-the-art imputation algorithms on three large-scale datasets. | [
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] |
Over the past decade explainable artificial intelligence has evolved from a
predominantly technical discipline into a field that is deeply intertwined with
social sciences. Insights such as human preference for contrastive -- more
precisely, counterfactual -- explanations have played a major role in this
transition, inspiring and guiding the research in computer science. Other
observations, while equally important, have received much less attention. The
desire of human explainees to communicate with artificial intelligence
explainers through a dialogue-like interaction has been mostly neglected by the
community. This poses many challenges for the effectiveness and widespread
adoption of such technologies as delivering a single explanation optimised
according to some predefined objectives may fail to engender understanding in
its recipients and satisfy their unique needs given the diversity of human
knowledge and intention. Using insights elaborated by Niklas Luhmann and, more
recently, Elena Esposito we apply social systems theory to highlight challenges
in explainable artificial intelligence and offer a path forward, striving to
reinvigorate the technical research in this direction. This paper aims to
demonstrate the potential of systems theoretical approaches to communication in
understanding problems and limitations of explainable artificial intelligence. | [
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] |
Adaptive regularization methods that exploit more than the diagonal entries
exhibit state of the art performance for many tasks, but can be prohibitive in
terms of memory and running time. We find the spectra of the Kronecker-factored
gradient covariance matrix in deep learning (DL) training tasks are
concentrated on a small leading eigenspace that changes throughout training,
motivating a low-rank sketching approach. We describe a generic method for
reducing memory and compute requirements of maintaining a matrix preconditioner
using the Frequent Directions (FD) sketch. While previous approaches have
explored applying FD for second-order optimization, we present a novel analysis
which allows efficient interpolation between resource requirements and the
degradation in regret guarantees with rank $k$: in the online convex
optimization (OCO) setting over dimension $d$, we match full-matrix $d^2$
memory regret using only $dk$ memory up to additive error in the bottom $d-k$
eigenvalues of the gradient covariance. Further, we show extensions of our work
to Shampoo, resulting in a method competitive in quality with Shampoo and Adam,
yet requiring only sub-linear memory for tracking second moments. | [
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] |
Local governments increasingly use artificial intelligence (AI) for automated
decision-making. Contestability, making systems responsive to dispute, is a way
to ensure they respect human rights to autonomy and dignity. We investigate the
design of public urban AI systems for contestability through the example of
camera cars: human-driven vehicles equipped with image sensors. Applying a
provisional framework for contestable AI, we use speculative design to create a
concept video of a contestable camera car. Using this concept video, we then
conduct semi-structured interviews with 17 civil servants who work with AI
employed by a large northwestern European city. The resulting data is analyzed
using reflexive thematic analysis to identify the main challenges facing the
implementation of contestability in public AI. We describe how civic
participation faces issues of representation, public AI systems should
integrate with existing democratic practices, and cities must expand capacities
for responsible AI development and operation. | [
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] |
One of the key capabilities of intelligent agents is the ability to discover
useful skills without external supervision. However, the current unsupervised
skill discovery methods are often limited to acquiring simple, easy-to-learn
skills due to the lack of incentives to discover more complex, challenging
behaviors. We introduce a novel unsupervised skill discovery method,
Controllability-aware Skill Discovery (CSD), which actively seeks complex,
hard-to-control skills without supervision. The key component of CSD is a
controllability-aware distance function, which assigns larger values to state
transitions that are harder to achieve with the current skills. Combined with
distance-maximizing skill discovery, CSD progressively learns more challenging
skills over the course of training as our jointly trained distance function
reduces rewards for easy-to-achieve skills. Our experimental results in six
robotic manipulation and locomotion environments demonstrate that CSD can
discover diverse complex skills including object manipulation and locomotion
skills with no supervision, significantly outperforming prior unsupervised
skill discovery methods. Videos and code are available at
https://seohong.me/projects/csd/ | [
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] |
Finding the best way to schedule operations in a computation graph is a
classical NP-hard problem which is central to compiler optimization. However,
evaluating the goodness of a schedule on the target hardware can be very
time-consuming. Traditional approaches as well as previous machine learning
ones typically optimize proxy metrics, which are fast to evaluate but can lead
to bad schedules when tested on the target hardware. In this work, we propose a
new approach to scheduling by sampling proportionally to the proxy metric using
a novel GFlowNet method. We introduce a technique to control the trade-off
between diversity and goodness of the proposed schedules at inference time and
demonstrate empirically that the pure optimization baselines can lead to subpar
performance with respect to our approach when tested on a target model.
Furthermore, we show that conditioning the GFlowNet on the computation graph
enables generalization to unseen scheduling problems for both synthetic and
real-world compiler datasets. | [
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] |
Silent speech interface is a promising technology that enables private
communications in natural language. However, previous approaches only support a
small and inflexible vocabulary, which leads to limited expressiveness. We
leverage contrastive learning to learn efficient lipreading representations,
enabling few-shot command customization with minimal user effort. Our model
exhibits high robustness to different lighting, posture, and gesture conditions
on an in-the-wild dataset. For 25-command classification, an F1-score of 0.8947
is achievable only using one shot, and its performance can be further boosted
by adaptively learning from more data. This generalizability allowed us to
develop a mobile silent speech interface empowered with on-device fine-tuning
and visual keyword spotting. A user study demonstrated that with LipLearner,
users could define their own commands with high reliability guaranteed by an
online incremental learning scheme. Subjective feedback indicated that our
system provides essential functionalities for customizable silent speech
interactions with high usability and learnability. | [
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] |
In this work, we advocate for the importance of singular learning theory
(SLT) as it pertains to the theory and practice of variational inference in
Bayesian neural networks (BNNs). To begin, using SLT, we lay to rest some of
the confusion surrounding discrepancies between downstream predictive
performance measured via e.g., the test log predictive density, and the
variational objective. Next, we use the SLT-corrected asymptotic form for
singular posterior distributions to inform the design of the variational family
itself. Specifically, we build upon the idealized variational family introduced
in \citet{bhattacharya_evidence_2020} which is theoretically appealing but
practically intractable. Our proposal takes shape as a normalizing flow where
the base distribution is a carefully-initialized generalized gamma. We conduct
experiments comparing this to the canonical Gaussian base distribution and show
improvements in terms of variational free energy and variational generalization
error. | [
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] |
Artificial neural networks are being proposed as models of parts of the
brain. The networks are compared to recordings of biological neurons, and good
performance in reproducing neural responses is considered to support the
model's validity. A key question is how much this system identification
approach tells us about brain computation. Does it validate one model
architecture over another? We evaluate the most commonly used comparison
techniques, such as a linear encoding model and centered kernel alignment, to
correctly identify a model by replacing brain recordings with known ground
truth models. System identification performance is quite variable; it also
depends significantly on factors independent of the ground truth architecture,
such as stimuli images. In addition, we show the limitations of using
functional similarity scores in identifying higher-level architectural motifs. | [
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] |
Is there a unified model for generating molecules considering different
conditions, such as binding pockets and chemical properties? Although
target-aware generative models have made significant advances in drug design,
they do not consider chemistry conditions and cannot guarantee the desired
chemical properties. Unfortunately, merging the target-aware and chemical-aware
models into a unified model to meet customized requirements may lead to the
problem of negative transfer. Inspired by the success of multi-task learning in
the NLP area, we use prefix embeddings to provide a novel generative model that
considers both the targeted pocket's circumstances and a variety of chemical
properties. All conditional information is represented as learnable features,
which the generative model subsequently employs as a contextual prompt.
Experiments show that our model exhibits good controllability in both single
and multi-conditional molecular generation. The controllability enables us to
outperform previous structure-based drug design methods. More interestingly, we
open up the attention mechanism and reveal coupling relationships between
conditions, providing guidance for multi-conditional molecule generation. | [
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] |
A deep reinforcement learning technique is presented for task offloading
decision-making algorithms for a multi-access edge computing (MEC) assisted
unmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT)
environment. The task offloading technique uses financial concepts such as cost
functions and conditional variable at risk (CVaR) in order to quantify the
damage that may be caused by each risky action. The approach was able to
quantify potential risks to train the reinforcement learning agent to avoid
risky behaviors that will lead to irreversible consequences for the farm. Such
consequences include an undetected fire, pest infestation, or a UAV being
unusable. The proposed CVaR-based technique was compared to other deep
reinforcement learning techniques and two fixed rule-based techniques. The
simulation results show that the CVaR-based risk quantifying method eliminated
the most dangerous risk, which was exceeding the deadline for a fire detection
task. As a result, it reduced the total number of deadline violations with a
negligible increase in energy consumption. | [
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Artificial intelligence (AI) governance is the body of standards and
practices used to ensure that AI systems are deployed responsibly. Current AI
governance approaches consist mainly of manual review and documentation
processes. While such reviews are necessary for many systems, they are not
sufficient to systematically address all potential harms, as they do not
operationalize governance requirements for system engineering, behavior, and
outcomes in a way that facilitates rigorous and reproducible evaluation. Modern
AI systems are data-centric: they act on data, produce data, and are built
through data engineering. The assurance of governance requirements must also be
carried out in terms of data. This work explores the systematization of
governance requirements via datasets and algorithmic evaluations. When applied
throughout the product lifecycle, data-centric governance decreases time to
deployment, increases solution quality, decreases deployment risks, and places
the system in a continuous state of assured compliance with governance
requirements. | [
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] |
Robotics, automation, and related Artificial Intelligence (AI) systems have
become pervasive bringing in concerns related to security, safety, accuracy,
and trust. With growing dependency on physical robots that work in close
proximity to humans, the security of these systems is becoming increasingly
important to prevent cyber-attacks that could lead to privacy invasion,
critical operations sabotage, and bodily harm. The current shortfall of
professionals who can defend such systems demands development and integration
of such a curriculum. This course description includes details about seven
self-contained and adaptive modules on "AI security threats against pervasive
robotic systems". Topics include: 1) Introduction, examples of attacks, and
motivation; 2) - Robotic AI attack surfaces and penetration testing; 3) -
Attack patterns and security strategies for input sensors; 4) - Training
attacks and associated security strategies; 5) - Inference attacks and
associated security strategies; 6) - Actuator attacks and associated security
strategies; and 7) - Ethics of AI, robotics, and cybersecurity. | [
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] |
Deep Learning (DL) acceleration support in CPUs has recently gained a lot of
traction, with several companies (Arm, Intel, IBM) announcing products with
specialized matrix engines accessible via GEMM instructions. CPUs are pervasive
and need to handle diverse requirements across DL workloads running in
edge/HPC/cloud platforms. Therefore, as DL workloads embrace sparsity to reduce
the computations and memory size of models, it is also imperative for CPUs to
add support for sparsity to avoid under-utilization of the dense matrix engine
and inefficient usage of the caches and registers. This work presents VEGETA, a
set of ISA and microarchitecture extensions over dense matrix engines to
support flexible structured sparsity for CPUs, enabling programmable support
for diverse DL models with varying degrees of sparsity. Compared to the
state-of-the-art (SOTA) dense matrix engine in CPUs, a VEGETA engine provides
1.09x, 2.20x, 3.74x, and 3.28x speed-ups when running 4:4 (dense), 2:4, 1:4,
and unstructured (95%) sparse DNN layers. | [
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Semi-supervised learning by self-training heavily relies on pseudo-label
selection (PLS). The selection often depends on the initial model fit on
labeled data. Early overfitting might thus be propagated to the final model by
selecting instances with overconfident but erroneous predictions, often
referred to as confirmation bias. This paper introduces BPLS, a Bayesian
framework for PLS that aims to mitigate this issue. At its core lies a
criterion for selecting instances to label: an analytical approximation of the
posterior predictive of pseudo-samples. We derive this selection criterion by
proving Bayes optimality of the posterior predictive of pseudo-samples. We
further overcome computational hurdles by approximating the criterion
analytically. Its relation to the marginal likelihood allows us to come up with
an approximation based on Laplace's method and the Gaussian integral. We
empirically assess BPLS for parametric generalized linear and non-parametric
generalized additive models on simulated and real-world data. When faced with
high-dimensional data prone to overfitting, BPLS outperforms traditional PLS
methods. | [
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] |
We model short-duration (e.g. day) trading in financial markets as a
sequential decision-making problem under uncertainty, with the added
complication of continual concept-drift. We, therefore, employ meta
reinforcement learning via the RL2 algorithm. It is also known that human
traders often rely on frequently occurring symbolic patterns in price series.
We employ logical program induction to discover symbolic patterns that occur
frequently as well as recently, and explore whether using such features
improves the performance of our meta reinforcement learning algorithm. We
report experiments on real data indicating that meta-RL is better than vanilla
RL and also benefits from learned symbolic features. | [
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] |
We survey recent work on machine learning (ML) techniques for selecting
cutting planes (or cuts) in mixed-integer linear programming (MILP). Despite
the availability of various classes of cuts, the task of choosing a set of cuts
to add to the linear programming (LP) relaxation at a given node of the
branch-and-bound (B&B) tree has defied both formal and heuristic solutions to
date. ML offers a promising approach for improving the cut selection process by
using data to identify promising cuts that accelerate the solution of MILP
instances. This paper presents an overview of the topic, highlighting recent
advances in the literature, common approaches to data collection, evaluation,
and ML model architectures. We analyze the empirical results in the literature
in an attempt to quantify the progress that has been made and conclude by
suggesting avenues for future research. | [
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] |
Understanding the skill sets and knowledge required for any career is of
utmost importance, but it is increasingly challenging in today's dynamic world
with rapid changes in terms of the tools and techniques used. Thus, it is
especially important to be able to accurately identify the required skill sets
for any job for better career insights and development. In this paper, we
propose and develop the Skill Recommendation (SkillRec) system for recommending
the relevant job skills required for a given job based on the job title.
SkillRec collects and identify the skill set required for a job based on the
job descriptions published by companies hiring for these roles. In addition to
the data collection and pre-processing capabilities, SkillRec also utilises
word/sentence embedding techniques for job title representation, alongside a
feed-forward neural network for job skill recommendation based on the job title
representation. Based on our preliminary experiments on a dataset of 6,000 job
titles and descriptions, SkillRec shows a promising performance in terms of
accuracy and F1-score. | [
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] |
Query categorization at customer-to-customer e-commerce platforms like
Facebook Marketplace is challenging due to the vagueness of search intent,
noise in real-world data, and imbalanced training data across languages. Its
deployment also needs to consider challenges in scalability and downstream
integration in order to translate modeling advances into better search result
relevance. In this paper we present HierCat, the query categorization system at
Facebook Marketplace. HierCat addresses these challenges by leveraging
multi-task pre-training of dual-encoder architectures with a hierarchical
inference step to effectively learn from weakly supervised training data mined
from searcher engagement. We show that HierCat not only outperforms popular
methods in offline experiments, but also leads to 1.4% improvement in NDCG and
4.3% increase in searcher engagement at Facebook Marketplace Search in online
A/B testing. | [
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] |
In this paper we present SurvLIMEpy, an open-source Python package that
implements the SurvLIME algorithm. This method allows to compute local feature
importance for machine learning algorithms designed for modelling Survival
Analysis data. Our implementation takes advantage of the parallelisation
paradigm as all computations are performed in a matrix-wise fashion which
speeds up execution time. Additionally, SurvLIMEpy assists the user with
visualization tools to better understand the result of the algorithm. The
package supports a wide variety of survival models, from the Cox Proportional
Hazards Model to deep learning models such as DeepHit or DeepSurv. Two types of
experiments are presented in this paper. First, by means of simulated data, we
study the ability of the algorithm to capture the importance of the features.
Second, we use three open source survival datasets together with a set of
survival algorithms in order to demonstrate how SurvLIMEpy behaves when applied
to different models. | [
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] |
The rise of mobile AI accelerators allows latency-sensitive applications to
execute lightweight Deep Neural Networks (DNNs) on the client side. However,
critical applications require powerful models that edge devices cannot host and
must therefore offload requests, where the high-dimensional data will compete
for limited bandwidth. This work proposes shifting away from focusing on
executing shallow layers of partitioned DNNs. Instead, it advocates
concentrating the local resources on variational compression optimized for
machine interpretability. We introduce a novel framework for resource-conscious
compression models and extensively evaluate our method in an environment
reflecting the asymmetric resource distribution between edge devices and
servers. Our method achieves 60% lower bitrate than a state-of-the-art SC
method without decreasing accuracy and is up to 16x faster than offloading with
existing codec standards. | [
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] |
With knowledge graphs (KGs) at the center of numerous applications such as
recommender systems and question answering, the need for generalized pipelines
to construct and continuously update such KGs is increasing. While the
individual steps that are necessary to create KGs from unstructured (e.g. text)
and structured data sources (e.g. databases) are mostly well-researched for
their one-shot execution, their adoption for incremental KG updates and the
interplay of the individual steps have hardly been investigated in a systematic
manner so far. In this work, we first discuss the main graph models for KGs and
introduce the major requirement for future KG construction pipelines. Next, we
provide an overview of the necessary steps to build high-quality KGs, including
cross-cutting topics such as metadata management, ontology development, and
quality assurance. We then evaluate the state of the art of KG construction
w.r.t the introduced requirements for specific popular KGs as well as some
recent tools and strategies for KG construction. Finally, we identify areas in
need of further research and improvement. | [
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] |
In sponsored search advertising (SSA), keywords serve as the basic unit of
business model, linking three stakeholders: consumers, advertisers and search
engines. This paper presents an overarching framework for keyword decisions
that highlights the touchpoints in search advertising management, including
four levels of keyword decisions, i.e., domain-specific keyword pool
generation, keyword targeting, keyword assignment and grouping, and keyword
adjustment. Using this framework, we review the state-of-the-art research
literature on keyword decisions with respect to techniques, input features and
evaluation metrics. Finally, we discuss evolving issues and identify potential
gaps that exist in the literature and outline novel research perspectives for
future exploration. | [
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] |
In this paper we study the problem of learning multi-step dynamics prediction
models (jumpy models) from unlabeled experience and their utility for fast
inference of (high-level) plans in downstream tasks. In particular we propose
to learn a jumpy model alongside a skill embedding space offline, from
previously collected experience for which no labels or reward annotations are
required. We then investigate several options of harnessing those learned
components in combination with model-based planning or model-free reinforcement
learning (RL) to speed up learning on downstream tasks. We conduct a set of
experiments in the RGB-stacking environment, showing that planning with the
learned skills and the associated model can enable zero-shot generalization to
new tasks, and can further speed up training of policies via reinforcement
learning. These experiments demonstrate that jumpy models which incorporate
temporal abstraction can facilitate planning in long-horizon tasks in which
standard dynamics models fail. | [
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] |
Invertible Neural Networks (INN) have become established tools for the
simulation and generation of highly complex data. We propose a quantum-gate
algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the
LHC data of jet-associated production of a Z-boson that decays into leptons, a
standard candle process for particle collider precision measurements. We
compare the QINN's performance for different loss functions and training
scenarios. For this task, we find that a hybrid QINN matches the performance of
a significantly larger purely classical INN in learning and generating complex
data. | [
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] |
Sim-and-real training is a promising alternative to sim-to-real training for
robot manipulations. However, the current sim-and-real training is neither
efficient, i.e., slow convergence to the optimal policy, nor effective, i.e.,
sizeable real-world robot data. Given limited time and hardware budgets, the
performance of sim-and-real training is not satisfactory. In this paper, we
propose a Consensus-based Sim-And-Real deep reinforcement learning algorithm
(CSAR) for manipulator pick-and-place tasks, which shows comparable performance
in both sim-and-real worlds. In this algorithm, we train the agents in
simulators and the real world to get the optimal policies for both sim-and-real
worlds. We found two interesting phenomenons: (1) Best policy in simulation is
not the best for sim-and-real training. (2) The more simulation agents, the
better sim-and-real training. The experimental video is available at:
https://youtu.be/mcHJtNIsTEQ. | [
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