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Imitation Learning from Observation (ILfO) is a setting in which a learner
tries to imitate the behavior of an expert, using only observational data and
without the direct guidance of demonstrated actions. In this paper, we
re-examine the use of optimal transport for IL, in which a reward is generated
based on the Wasserstein distance between the state trajectories of the learner
and expert. We show that existing methods can be simplified to generate a
reward function without requiring learned models or adversarial learning.
Unlike many other state-of-the-art methods, our approach can be integrated with
any RL algorithm, and is amenable to ILfO. We demonstrate the effectiveness of
this simple approach on a variety of continuous control tasks and find that it
surpasses the state of the art in the IlfO setting, achieving expert-level
performance across a range of evaluation domains even when observing only a
single expert trajectory without actions. | [
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] |
We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challenges
agents to use reasoning and decision-making skills to solve complex activities
that resemble everyday human challenges. The Mini-BEHAVIOR environment is a
fast, realistic Gridworld environment that offers the benefits of rapid
prototyping and ease of use while preserving a symbolic level of physical
realism and complexity found in complex embodied AI benchmarks. We introduce
key features such as procedural generation, to enable the creation of countless
task variations and support open-ended learning. Mini-BEHAVIOR provides
implementations of various household tasks from the original BEHAVIOR
benchmark, along with starter code for data collection and reinforcement
learning agent training. In essence, Mini-BEHAVIOR offers a fast, open-ended
benchmark for evaluating decision-making and planning solutions in embodied AI.
It serves as a user-friendly entry point for research and facilitates the
evaluation and development of solutions, simplifying their assessment and
development while advancing the field of embodied AI. Code is publicly
available at https://github.com/StanfordVL/mini_behavior. | [
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] |
Highly autonomous generative agents powered by large language models promise
to simulate intricate social behaviors in virtual societies. However, achieving
real-time interactions with humans at a low computational cost remains
challenging. Here, we introduce Lyfe Agents. They combine low-cost with
real-time responsiveness, all while remaining intelligent and goal-oriented.
Key innovations include: (1) an option-action framework, reducing the cost of
high-level decisions; (2) asynchronous self-monitoring for better
self-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizing
critical memory items at a low cost. We evaluate Lyfe Agents' self-motivation
and sociability across several multi-agent scenarios in our custom LyfeGame 3D
virtual environment platform. When equipped with our brain-inspired techniques,
Lyfe Agents can exhibit human-like self-motivated social reasoning. For
example, the agents can solve a crime (a murder mystery) through autonomous
collaboration and information exchange. Meanwhile, our techniques enabled Lyfe
Agents to operate at a computational cost 10-100 times lower than existing
alternatives. Our findings underscore the transformative potential of
autonomous generative agents to enrich human social experiences in virtual
worlds. | [
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] |
Although pervasive spread of misinformation on social media platforms has
become a pressing challenge, existing platform interventions have shown limited
success in curbing its dissemination. In this study, we propose a stance-aware
graph neural network (stance-aware GNN) that leverages users' stances to
proactively predict misinformation spread. As different user stances can form
unique echo chambers, we customize four information passing paths in
stance-aware GNN, while the trainable attention weights provide explainability
by highlighting each structure's importance. Evaluated on a real-world dataset,
stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs
without user stance by over 4.69%. Furthermore, the attention weights indicate
that users' opposition stances have a higher impact on their neighbors'
behaviors than supportive ones, which function as social correction to halt
misinformation propagation. Overall, our study provides an effective predictive
model for platforms to combat misinformation, and highlights the impact of user
stances in the misinformation propagation. | [
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] |
We present AstroCLIP, a strategy to facilitate the construction of
astronomical foundation models that bridge the gap between diverse
observational modalities. We demonstrate that a cross-modal contrastive
learning approach between images and optical spectra of galaxies yields highly
informative embeddings of both modalities. In particular, we apply our method
on multi-band images and optical spectra from the Dark Energy Spectroscopic
Instrument (DESI), and show that: (1) these embeddings are well-aligned between
modalities and can be used for accurate cross-modal searches, and (2) these
embeddings encode valuable physical information about the galaxies -- in
particular redshift and stellar mass -- that can be used to achieve competitive
zero- and few- shot predictions without further finetuning. Additionally, in
the process of developing our approach, we also construct a novel,
transformer-based model and pretraining approach for processing galaxy spectra. | [
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Sparse deep learning has become a popular technique for improving the
performance of deep neural networks in areas such as uncertainty
quantification, variable selection, and large-scale network compression.
However, most existing research has focused on problems where the observations
are independent and identically distributed (i.i.d.), and there has been little
work on the problems where the observations are dependent, such as time series
data and sequential data in natural language processing. This paper aims to
address this gap by studying the theory for sparse deep learning with dependent
data. We show that sparse recurrent neural networks (RNNs) can be consistently
estimated, and their predictions are asymptotically normally distributed under
appropriate assumptions, enabling the prediction uncertainty to be correctly
quantified. Our numerical results show that sparse deep learning outperforms
state-of-the-art methods, such as conformal predictions, in prediction
uncertainty quantification for time series data. Furthermore, our results
indicate that the proposed method can consistently identify the autoregressive
order for time series data and outperform existing methods in large-scale model
compression. Our proposed method has important practical implications in fields
such as finance, healthcare, and energy, where both accurate point estimates
and prediction uncertainty quantification are of concern. | [
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] |
This work investigates the predictive potential of bipolar
electroencephalogram (EEG) recordings towards efficient prediction of poor
neurological outcomes. A retrospective design using a hybrid deep learning
approach is utilized to optimize an objective function aiming for high
specificity, i.e., true positive rate (TPR) with reduced false positives (<
0.05). A multi-channel EEG array of 18 bipolar channel pairs from a randomly
selected 5-minute segment in an hour is kept. In order to determine the outcome
prediction, a combination of a feature encoder with 1-D convolutional layers,
learnable position encoding, a context network with attention mechanisms, and
finally, a regressor and classifier blocks are used. The feature encoder
extricates local temporal and spatial features, while the following position
encoding and attention mechanisms attempt to capture global temporal
dependencies. Results: The proposed framework by our team, OUS IVS, when
validated on the challenge hidden validation data, exhibited a score of 0.57. | [
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] |
The extensive ubiquitous availability of sensors in smart devices and the
Internet of Things (IoT) has opened up the possibilities for implementing
sensor-based activity recognition. As opposed to traditional sensor time-series
processing and hand-engineered feature extraction, in light of deep learning's
proven effectiveness across various domains, numerous deep methods have been
explored to tackle the challenges in activity recognition, outperforming the
traditional signal processing and traditional machine learning approaches. In
this work, by performing extensive experimental studies on two human activity
recognition datasets, we investigate the performance of common deep learning
and machine learning approaches as well as different training mechanisms (such
as contrastive learning), and various feature representations extracted from
the sensor time-series data and measure their effectiveness for the human
activity recognition task. | [
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] |
On-device machine learning (ML) promises to improve the privacy,
responsiveness, and proliferation of new, intelligent user experiences by
moving ML computation onto everyday personal devices. However, today's large ML
models must be drastically compressed to run efficiently on-device, a hurtle
that requires deep, yet currently niche expertise. To engage the broader
human-centered ML community in on-device ML experiences, we present the results
from an interview study with 30 experts at Apple that specialize in producing
efficient models. We compile tacit knowledge that experts have developed
through practical experience with model compression across different hardware
platforms. Our findings offer pragmatic considerations missing from prior work,
covering the design process, trade-offs, and technical strategies that go into
creating efficient models. Finally, we distill design recommendations for
tooling to help ease the difficulty of this work and bring on-device ML into to
more widespread practice. | [
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] |
Recommender systems have emerged as a crucial component of the modern web
ecosystem. The effectiveness and accuracy of such systems are critical for
providing users with personalized recommendations that meet their specific
interests and needs. In this paper, we introduce a novel model that utilizes a
Graph Neural Network (GNN) in conjunction with sentence transformer embeddings
to predict anime recommendations for different users. Our model employs the
task of link prediction to create a recommendation system that considers both
the features of anime and user interactions with different anime. The
hybridization of the GNN and transformer embeddings enables us to capture both
inter-level and intra-level features of anime data.Our model not only
recommends anime to users but also predicts the rating a specific user would
give to an anime. We utilize the GraphSAGE network for model building and
weighted root mean square error (RMSE) to evaluate the performance of the
model. Our approach has the potential to significantly enhance the accuracy and
effectiveness of anime recommendation systems and can be extended to other
domains that require personalized recommendations. | [
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] |
Diffusion-based generative models in SE(3)-invariant space have demonstrated
promising performance in molecular conformation generation, but typically
require solving stochastic differential equations (SDEs) with thousands of
update steps. Till now, it remains unclear how to effectively accelerate this
procedure explicitly in SE(3)-invariant space, which greatly hinders its wide
application in the real world. In this paper, we systematically study the
diffusion mechanism in SE(3)-invariant space via the lens of approximate errors
induced by existing methods. Thereby, we develop more precise approximate in
SE(3) in the context of projected differential equations. Theoretical analysis
is further provided as well as empirical proof relating hyper-parameters with
such errors. Altogether, we propose a novel acceleration scheme for generating
molecular conformations in SE(3)-invariant space. Experimentally, our scheme
can generate high-quality conformations with 50x--100x speedup compared to
existing methods. | [
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] |
The capability to transfer mastered skills to accomplish a range of similar
yet novel tasks is crucial for intelligent robots. In this work, we introduce
$\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics
simulation to efficiently transfer robotic skills. Specifically,
$\textit{Diff-Transfer}$ discovers a feasible path within the task space that
brings the source task to the target task. At each pair of adjacent points
along this task path, which is two sub-tasks, $\textit{Diff-Transfer}$ adapts
known actions from one sub-task to tackle the other sub-task successfully. The
adaptation is guided by the gradient information from differentiable physics
simulations. We propose a novel path-planning method to generate sub-tasks,
leveraging $Q$-learning with a task-level state and reward. We implement our
framework in simulation experiments and execute four challenging transfer tasks
on robotic manipulation, demonstrating the efficacy of $\textit{Diff-Transfer}$
through comprehensive experiments. Supplementary and Videos are on the website
https://sites.google.com/view/difftransfer | [
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] |
In the context of inferring a Bayesian network structure (directed acyclic
graph, DAG for short), we devise a non-reversible continuous time Markov chain,
the "Causal Zig-Zag sampler", that targets a probability distribution over
classes of observationally equivalent (Markov equivalent) DAGs. The classes are
represented as completed partially directed acyclic graphs (CPDAGs). The
non-reversible Markov chain relies on the operators used in Chickering's Greedy
Equivalence Search (GES) and is endowed with a momentum variable, which
improves mixing significantly as we show empirically. The possible target
distributions include posterior distributions based on a prior over DAGs and a
Markov equivalent likelihood. We offer an efficient implementation wherein we
develop new algorithms for listing, counting, uniformly sampling, and applying
possible moves of the GES operators, all of which significantly improve upon
the state-of-the-art. | [
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] |
Deep generative models are key-enabling technology to computer vision, text
generation and large language models. Denoising diffusion probabilistic models
(DDPMs) have recently gained much attention due to their ability to generate
diverse and high-quality samples in many computer vision tasks, as well as to
incorporate flexible model architectures and relatively simple training scheme.
Quantum generative models, empowered by entanglement and superposition, have
brought new insight to learning classical and quantum data. Inspired by the
classical counterpart, we propose the quantum denoising diffusion probabilistic
models (QuDDPM) to enable efficiently trainable generative learning of quantum
data. QuDDPM adopts sufficient layers of circuits to guarantee expressivity,
while introduces multiple intermediate training tasks as interpolation between
the target distribution and noise to avoid barren plateau and guarantee
efficient training. We demonstrate QuDDPM's capability in learning correlated
quantum noise model and learning topological structure of nontrivial
distribution of quantum data. | [
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] |
The development of ethical AI systems is currently geared toward setting
objective functions that align with human objectives. However, finding such
functions remains a research challenge, while in RL, setting rewards by hand is
a fairly standard approach. We present a methodology for dynamic value
alignment, where the values that are to be aligned with are dynamically
changing, using a multiple-objective approach. We apply this approach to extend
Deep $Q$-Learning to accommodate multiple objectives and evaluate this method
on a simplified two-leg intersection controlled by a switching agent.Our
approach dynamically accommodates the preferences of drivers on the system and
achieves better overall performance across three metrics (speeds, stops, and
waits) while integrating objectives that have competing or conflicting actions. | [
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] |
Recent advancements in deep learning techniques have opened new possibilities
for designing solutions for autonomous cyber defence. Teams of intelligent
agents in computer network defence roles may reveal promising avenues to
safeguard cyber and kinetic assets. In a simulated game environment, agents are
evaluated on their ability to jointly mitigate attacker activity in host-based
defence scenarios. Defender systems are evaluated against heuristic attackers
with the goals of compromising network confidentiality, integrity, and
availability. Value-based Independent Learning and Centralized Training
Decentralized Execution (CTDE) cooperative Multi-Agent Reinforcement Learning
(MARL) methods are compared revealing that both approaches outperform a simple
multi-agent heuristic defender. This work demonstrates the ability of
cooperative MARL to learn effective cyber defence tactics against varied
threats. | [
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] |
Backdoor adjustment is a technique in causal inference for estimating
interventional quantities from purely observational data. For example, in
medical settings, backdoor adjustment can be used to control for confounding
and estimate the effectiveness of a treatment. However, high dimensional
treatments and confounders pose a series of potential pitfalls: tractability,
identifiability, optimization. In this work, we take a generative modeling
approach to backdoor adjustment for high dimensional treatments and
confounders. We cast backdoor adjustment as an optimization problem in
variational inference without reliance on proxy variables and hidden
confounders. Empirically, our method is able to estimate interventional
likelihood in a variety of high dimensional settings, including semi-synthetic
X-ray medical data. To the best of our knowledge, this is the first application
of backdoor adjustment in which all the relevant variables are high
dimensional. | [
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] |
Speech separation remains an important topic for multi-speaker technology
researchers. Convolution augmented transformers (conformers) have performed
well for many speech processing tasks but have been under-researched for speech
separation. Most recent state-of-the-art (SOTA) separation models have been
time-domain audio separation networks (TasNets). A number of successful models
have made use of dual-path (DP) networks which sequentially process local and
global information. Time domain conformers (TD-Conformers) are an analogue of
the DP approach in that they also process local and global context sequentially
but have a different time complexity function. It is shown that for realistic
shorter signal lengths, conformers are more efficient when controlling for
feature dimension. Subsampling layers are proposed to further improve
computational efficiency. The best TD-Conformer achieves 14.6 dB and 21.2 dB
SISDR improvement on the WHAMR and WSJ0-2Mix benchmarks, respectively. | [
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] |
AI models are increasing in size and recent advancement in the community has
shown that unlike HPC applications where double precision datatype are
required, lower-precision datatypes such as fp8 or int4 are sufficient to bring
the same model quality both for training and inference. Following these trends,
GPU vendors such as NVIDIA and AMD have added hardware support for fp16, fp8
and int8 GeMM operations with an exceptional performance via Tensor Cores.
However, this paper proposes a new algorithm called msGeMM which shows that AI
models with low-precision datatypes can run with ~2.5x fewer multiplication and
add instructions. Efficient implementation of this algorithm requires special
CUDA cores with the ability to add elements from a small look-up table at the
rate of Tensor Cores. | [
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] |
Good posture and form are essential for safe and productive exercising. Even
in gym settings, trainers may not be readily available for feedback.
Rehabilitation therapies and fitness workouts can thus benefit from recommender
systems that provide real-time evaluation. In this paper, we present an
algorithmic pipeline that can diagnose problems in exercise techniques and
offer corrective recommendations, with high sensitivity and specificity in
real-time. We use MediaPipe for pose recognition, count repetitions using
peak-prominence detection, and use a learnable physics simulator to track
motion evolution for each exercise. A test video is diagnosed based on
deviations from the prototypical learned motion using statistical learning. The
system is evaluated on six full and upper body exercises. These real-time
recommendations, counseled via low-cost equipment like smartphones, will allow
exercisers to rectify potential mistakes making self-practice feasible while
reducing the risk of workout injuries. | [
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] |
Fatigue crack growth is one of the most common types of deterioration in
metal structures with significant implications on their reliability. Recent
advances in Structural Health Monitoring (SHM) have motivated the use of
structural response data to predict future crack growth under uncertainty, in
order to enable a transition towards predictive maintenance. Accurately
representing different sources of uncertainty in stochastic crack growth (SCG)
processes is a non-trivial task. The present work builds on previous research
on physics-based SCG modeling under both material and load-related uncertainty.
The aim here is to construct computationally efficient, probabilistic surrogate
models for SCG processes that successfully encode these different sources of
uncertainty. An approach inspired by latent variable modeling is employed that
utilizes Gaussian Process (GP) regression models to enable the surrogates to be
used to generate prior distributions for different Bayesian SHM tasks as the
application of interest. Implementation is carried out in a numerical setting
and model performance is assessed for two fundamental crack SHM problems;
namely crack length monitoring (damage quantification) and crack growth
monitoring (damage prognosis). | [
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] |
This work proposes a novel approach to social robot navigation by learning to
generate robot controls from a social motion latent space. By leveraging this
social motion latent space, the proposed method achieves significant
improvements in social navigation metrics such as success rate, navigation
time, and trajectory length while producing smoother (less jerk and angular
deviations) and more anticipatory trajectories. The superiority of the proposed
method is demonstrated through comparison with baseline models in various
scenarios. Additionally, the concept of humans' awareness towards the robot is
introduced into the social robot navigation framework, showing that
incorporating human awareness leads to shorter and smoother trajectories owing
to humans' ability to positively interact with the robot. | [
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] |
Knowledge Graphs (KG) are the backbone of many data-intensive applications
since they can represent data coupled with its meaning and context. Aligning
KGs across different domains and providers is necessary to afford a fuller and
integrated representation. A severe limitation of current KG alignment (KGA)
algorithms is that they fail to articulate logical thinking and reasoning with
lexical, structural, and semantic data learning. Deep learning models are
increasingly popular for KGA inspired by their good performance in other tasks,
but they suffer from limitations in explainability, reasoning, and data
efficiency. Hybrid neurosymbolic learning models hold the promise of
integrating logical and data perspectives to produce high-quality alignments
that are explainable and support validation through human-centric approaches.
This paper examines the current state of the art in KGA and explores the
potential for neurosymbolic integration, highlighting promising research
directions for combining these fields. | [
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] |
Rollator walkers allow people with physical limitations to increase their
mobility and give them the confidence and independence to participate in
society for longer. However, rollator walker users often have poor posture,
leading to further health problems and, in the worst case, falls. Integrating
sensors into rollator walker designs can help to address this problem and
results in a platform that allows several other interesting use cases. This
paper briefly overviews existing systems and the current research directions
and challenges in this field. We also present our early HealthWalk rollator
walker prototype for data collection with older people, rheumatism, multiple
sclerosis and Parkinson patients, and individuals with visual impairments. | [
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] |
Geological carbon and energy storage are pivotal for achieving net-zero
carbon emissions and addressing climate change. However, they face
uncertainties due to geological factors and operational limitations, resulting
in possibilities of induced seismic events or groundwater contamination. To
overcome these challenges, we propose a specialized machine-learning (ML) model
to manage extensive reservoir models efficiently.
While ML approaches hold promise for geological carbon storage, the
substantial computational resources required for large-scale analysis are the
obstacle. We've developed a method to reduce the training cost for deep neural
operator models, using domain decomposition and a topology embedder to link
spatio-temporal points. This approach allows accurate predictions within the
model's domain, even for untrained data, enhancing ML efficiency for
large-scale geological storage applications. | [
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] |
Counterfactuals can offer valuable insights by answering what would have been
observed under altered circumstances, conditional on a factual observation.
Whereas the classical interventional interpretation of counterfactuals has been
studied extensively, backtracking constitutes a less studied alternative the
backtracking principle has emerged as an alternative philosophy where all
causal laws are kept intact. In the present work, we introduce a practical
method for computing backtracking counterfactuals in structural causal models
that consist of deep generative components. To this end, we impose conditions
on the structural assignments that enable the generation of counterfactuals by
solving a tractable constrained optimization problem in the structured latent
space of a causal model. Our formulation also facilitates a comparison with
methods in the field of counterfactual explanations. Compared to these, our
method represents a versatile, modular and causally compliant alternative. We
demonstrate these properties experimentally on a modified version of MNIST and
CelebA. | [
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] |
Symbolic music is widely used in various deep learning tasks, including
generation, transcription, synthesis, and Music Information Retrieval (MIR). It
is mostly employed with discrete models like Transformers, which require music
to be tokenized, i.e., formatted into sequences of distinct elements called
tokens. Tokenization can be performed in different ways. As Transformer can
struggle at reasoning, but capture more easily explicit information, it is
important to study how the way the information is represented for such model
impact their performances. In this work, we analyze the common tokenization
methods and experiment with time and note duration representations. We compare
the performances of these two impactful criteria on several tasks, including
composer and emotion classification, music generation, and sequence
representation learning. We demonstrate that explicit information leads to
better results depending on the task. | [
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] |
Recent advancements in AI foundation models have made it possible for them to
be utilized off-the-shelf for creative tasks, including ideating design
concepts or generating visual prototypes. However, integrating these models
into the creative process can be challenging as they often exist as standalone
applications tailored to specific tasks. To address this challenge, we
introduce Jigsaw, a prototype system that employs puzzle pieces as metaphors to
represent foundation models. Jigsaw allows designers to combine different
foundation model capabilities across various modalities by assembling
compatible puzzle pieces. To inform the design of Jigsaw, we interviewed ten
designers and distilled design goals. In a user study, we showed that Jigsaw
enhanced designers' understanding of available foundation model capabilities,
provided guidance on combining capabilities across different modalities and
tasks, and served as a canvas to support design exploration, prototyping, and
documentation. | [
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] |
In this paper, we explore the challenges associated with navigating complex
T-intersections in dense traffic scenarios for autonomous vehicles (AVs).
Reinforcement learning algorithms have emerged as a promising approach to
address these challenges by enabling AVs to make safe and efficient decisions
in real-time. Here, we address the problem of efficiently and safely navigating
T-intersections using a lower-cost, single-agent approach based on the Twin
Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning
algorithm. We show that our TD3-based method, when trained and tested in the
CARLA simulation platform, demonstrates stable convergence and improved safety
performance in various traffic densities. Our results reveal that the proposed
approach enables the AV to effectively navigate T-intersections, outperforming
previous methods in terms of travel delays, collision minimization, and overall
cost. This study contributes to the growing body of knowledge on reinforcement
learning applications in autonomous driving and highlights the potential of
single-agent, cost-effective methods for addressing more complex driving
scenarios and advancing reinforcement learning algorithms in the future. | [
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] |
Reinforcement learning (RL) using world models has found significant recent
successes. However, when a sudden change to world mechanics or properties
occurs then agent performance and reliability can dramatically decline. We
refer to the sudden change in visual properties or state transitions as {\em
novelties}. Implementing novelty detection within generated world model
frameworks is a crucial task for protecting the agent when deployed. In this
paper, we propose straightforward bounding approaches to incorporate novelty
detection into world model RL agents, by utilizing the misalignment of the
world model's hallucinated states and the true observed states as an anomaly
score. We first provide an ontology of novelty detection relevant to sequential
decision making, then we provide effective approaches to detecting novelties in
a distribution of transitions learned by an agent in a world model. Finally, we
show the advantage of our work in a novel environment compared to traditional
machine learning novelty detection methods as well as currently accepted RL
focused novelty detection algorithms. | [
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] |
Despite recent progress in Reinforcement Learning for robotics applications,
many tasks remain prohibitively difficult to solve because of the expensive
interaction cost. Transfer learning helps reduce the training time in the
target domain by transferring knowledge learned in a source domain. Sim2Real
transfer helps transfer knowledge from a simulated robotic domain to a physical
target domain. Knowledge transfer reduces the time required to train a task in
the physical world, where the cost of interactions is high. However, most
existing approaches assume exact correspondence in the task structure and the
physical properties of the two domains. This work proposes a framework for
Few-Shot Policy Transfer between two domains through Observation Mapping and
Behavior Cloning. We use Generative Adversarial Networks (GANs) along with a
cycle-consistency loss to map the observations between the source and target
domains and later use this learned mapping to clone the successful source task
behavior policy to the target domain. We observe successful behavior policy
transfer with limited target task interactions and in cases where the source
and target task are semantically dissimilar. | [
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] |
Community detection techniques are useful tools for social media platforms to
discover tightly connected groups of users who share common interests. However,
this functionality often comes at the expense of potentially exposing
individuals to privacy breaches by inadvertently revealing their tastes or
preferences. Therefore, some users may wish to safeguard their anonymity and
opt out of community detection for various reasons, such as affiliation with
political or religious organizations.
In this study, we address the challenge of community membership hiding, which
involves strategically altering the structural properties of a network graph to
prevent one or more nodes from being identified by a given community detection
algorithm. We tackle this problem by formulating it as a constrained
counterfactual graph objective, and we solve it via deep reinforcement
learning. We validate the effectiveness of our method through two distinct
tasks: node and community deception. Extensive experiments show that our
approach overall outperforms existing baselines in both tasks. | [
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] |
The ability to plan at many different levels of abstraction enables agents to
envision the long-term repercussions of their decisions and thus enables
sample-efficient learning. This becomes particularly beneficial in complex
environments from high-dimensional state space such as pixels, where the goal
is distant and the reward sparse. We introduce Forecaster, a deep hierarchical
reinforcement learning approach which plans over high-level goals leveraging a
temporally abstract world model. Forecaster learns an abstract model of its
environment by modelling the transitions dynamics at an abstract level and
training a world model on such transition. It then uses this world model to
choose optimal high-level goals through a tree-search planning procedure. It
additionally trains a low-level policy that learns to reach those goals. Our
method not only captures building world models with longer horizons, but also,
planning with such models in downstream tasks. We empirically demonstrate
Forecaster's potential in both single-task learning and generalization to new
tasks in the AntMaze domain. | [
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] |
The development of closed-loop systems for glycemia control in type I
diabetes relies heavily on simulated patients. Improving the performances and
adaptability of these close-loops raises the risk of over-fitting the
simulator. This may have dire consequences, especially in unusual cases which
were not faithfully-if at all-captured by the simulator. To address this, we
propose to use offline RL agents, trained on real patient data, to perform the
glycemia control. To further improve the performances, we propose an end-to-end
personalization pipeline, which leverages offline-policy evaluation methods to
remove altogether the need of a simulator, while still enabling an estimation
of clinically relevant metrics for diabetes. | [
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] |
In recent years, citizen science has become a larger and larger part of the
scientific community. Its ability to crowd source data and expertise from
thousands of citizen scientists makes it invaluable. Despite the field's
growing popularity, the interactions and structure of citizen science projects
are still poorly understood and under analyzed. We use the iNaturalist citizen
science platform as a case study to analyze the structure of citizen science
projects. We frame the data from iNaturalist as a bipartite network and use
visualizations as well as established network science techniques to gain
insights into the structure and interactions between users in citizen science
projects. Finally, we propose a novel unique benchmark for network science
research by using the iNaturalist data to create a network which has an unusual
structure relative to other common benchmark networks. We demonstrate using a
link prediction task that this network can be used to gain novel insights into
a variety of network science methods. | [
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] |
The growing dependence on machine learning in real-world applications
emphasizes the importance of understanding and ensuring its safety. Backdoor
attacks pose a significant security risk due to their stealthy nature and
potentially serious consequences. Such attacks involve embedding triggers
within a learning model with the intention of causing malicious behavior when
an active trigger is present while maintaining regular functionality without
it. This paper evaluates the effectiveness of any backdoor attack incorporating
a constant trigger, by establishing tight lower and upper boundaries for the
performance of the compromised model on both clean and backdoor test data. The
developed theory answers a series of fundamental but previously underexplored
problems, including (1) what are the determining factors for a backdoor
attack's success, (2) what is the direction of the most effective backdoor
attack, and (3) when will a human-imperceptible trigger succeed. Our derived
understanding applies to both discriminative and generative models. We also
demonstrate the theory by conducting experiments using benchmark datasets and
state-of-the-art backdoor attack scenarios. | [
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] |
This report focuses on enhancing a binary code comment quality classification
model by integrating generated code and comment pairs, to improve model
accuracy. The dataset comprises 9048 pairs of code and comments written in the
C programming language, each annotated as "Useful" or "Not Useful."
Additionally, code and comment pairs are generated using a Large Language Model
Architecture, and these generated pairs are labeled to indicate their utility.
The outcome of this effort consists of two classification models: one utilizing
the original dataset and another incorporating the augmented dataset with the
newly generated code comment pairs and labels. | [
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] |
In this work, we explore how a real time reading tracker can be built
efficiently for children's voices. While previously proposed reading trackers
focused on ASR-based cascaded approaches, we propose a fully end-to-end model
making it less prone to lags in voice tracking. We employ a pointer network
that directly learns to predict positions in the ground truth text conditioned
on the streaming speech. To train this pointer network, we generate ground
truth training signals by using forced alignment between the read speech and
the text being read on the training set. Exploring different forced alignment
models, we find a neural attention based model is at least as close in
alignment accuracy to the Montreal Forced Aligner, but surprisingly is a better
training signal for the pointer network. Our results are reported on one adult
speech data (TIMIT) and two children's speech datasets (CMU Kids and Reading
Races). Our best model can accurately track adult speech with 87.8% accuracy
and the much harder and disfluent children's speech with 77.1% accuracy on CMU
Kids data and a 65.3% accuracy on the Reading Races dataset. | [
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Self-play is a technique for machine learning in multi-agent systems where a
learning algorithm learns by interacting with copies of itself. Self-play is
useful for generating large quantities of data for learning, but has the
drawback that the agents the learner will face post-training may have
dramatically different behavior than the learner came to expect by interacting
with itself. For the special case of two-player constant-sum games, self-play
that reaches Nash equilibrium is guaranteed to produce strategies that perform
well against any post-training opponent; however, no such guarantee exists for
multiplayer games. We show that in games that approximately decompose into a
set of two-player constant-sum games (called constant-sum polymatrix games)
where global $\epsilon$-Nash equilibria are boundedly far from Nash equilibria
in each subgame (called subgame stability), any no-external-regret algorithm
that learns by self-play will produce a strategy with bounded vulnerability.
For the first time, our results identify a structural property of multiplayer
games that enable performance guarantees for the strategies produced by a broad
class of self-play algorithms. We demonstrate our findings through experiments
on Leduc poker. | [
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] |
Robots need to estimate the material and dynamic properties of objects from
observations in order to simulate them accurately. We present a Bayesian
optimization approach to identifying the material property parameters of
objects based on a set of observations. Our focus is on estimating these
properties based on observations of scenes with different sets of interacting
objects. We propose an approach that exploits the structure of the reward
function by modeling the reward for each observation separately and using only
the parameters of the objects in that scene as inputs. The resulting
lower-dimensional models generalize better over the parameter space, which in
turn results in a faster optimization. To speed up the optimization process
further, and reduce the number of simulation runs needed to find good parameter
values, we also propose partial evaluations of the reward function, wherein the
selected parameters are only evaluated on a subset of real world evaluations.
The approach was successfully evaluated on a set of scenes with a wide range of
object interactions, and we showed that our method can effectively perform
incremental learning without resetting the rewards of the gathered
observations. | [
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] |
Consider the following problem: given a few demonstrations of a task across a
few different objects, how can a robot learn to perform that same task on new,
previously unseen objects? This is challenging because the large variety of
objects within a class makes it difficult to infer the task-relevant
relationship between the new objects and the objects in the demonstrations. We
address this by formulating imitation learning as a conditional alignment
problem between graph representations of objects. Consequently, we show that
this conditioning allows for in-context learning, where a robot can perform a
task on a set of new objects immediately after the demonstrations, without any
prior knowledge about the object class or any further training. In our
experiments, we explore and validate our design choices, and we show that our
method is highly effective for few-shot learning of several real-world,
everyday tasks, whilst outperforming baselines. Videos are available on our
project webpage at https://www.robot-learning.uk/implicit-graph-alignment. | [
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] |
Diffusion models have risen as a powerful tool in robotics due to their
flexibility and multi-modality. While some of these methods effectively address
complex problems, they often depend heavily on inference-time obstacle
detection and require additional equipment. Addressing these challenges, we
present a method that, during inference time, simultaneously generates only
reachable goals and plans motions that avoid obstacles, all from a single
visual input. Central to our approach is the novel use of a collision-avoiding
diffusion kernel for training. Through evaluations against behavior-cloning and
classical diffusion models, our framework has proven its robustness. It is
particularly effective in multi-modal environments, navigating toward goals and
avoiding unreachable ones blocked by obstacles, while ensuring collision
avoidance. | [
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] |
This paper presents a novel approach to enhance the performance of binary
code comment quality classification models through the application of
Generative Artificial Intelligence (AI). By leveraging the OpenAI API, a
dataset comprising 1239 newly generated code-comment pairs, extracted from
various GitHub repositories and open-source projects, has been labelled as
"Useful" or "Not Useful", and integrated into the existing corpus of 9048 pairs
in the C programming language. Employing a cutting-edge Large Language Model
Architecture, the generated dataset demonstrates notable improvements in model
accuracy. Specifically, when incorporated into the Support Vector Machine (SVM)
model, a 6% increase in precision is observed, rising from 0.79 to 0.85.
Additionally, the Artificial Neural Network (ANN) model exhibits a 1.5%
increase in recall, climbing from 0.731 to 0.746. This paper sheds light on the
potential of Generative AI in augmenting code comment quality classification
models. The results affirm the effectiveness of this methodology, indicating
its applicability in broader contexts within software development and quality
assurance domains. The findings underscore the significance of integrating
generative techniques to advance the accuracy and efficacy of machine learning
models in practical software engineering scenarios. | [
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] |
Nowadays, transportation networks face the challenge of sub-optimal control
policies that can have adverse effects on human health, the environment, and
contribute to traffic congestion. Increased levels of air pollution and
extended commute times caused by traffic bottlenecks make intersection traffic
signal controllers a crucial component of modern transportation infrastructure.
Despite several adaptive traffic signal controllers in literature, limited
research has been conducted on their comparative performance. Furthermore,
despite carbon dioxide (CO2) emissions' significance as a global issue, the
literature has paid limited attention to this area. In this report, we propose
EcoLight, a reward shaping scheme for reinforcement learning algorithms that
not only reduces CO2 emissions but also achieves competitive results in metrics
such as travel time. We compare the performance of tabular Q-Learning, DQN,
SARSA, and A2C algorithms using metrics such as travel time, CO2 emissions,
waiting time, and stopped time. Our evaluation considers multiple scenarios
that encompass a range of road users (trucks, buses, cars) with varying
pollution levels. | [
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] |
Seamless human-robot manipulation in close proximity relies on accurate
forecasts of human motion. While there has been significant progress in
learning forecast models at scale, when applied to manipulation tasks, these
models accrue high errors at critical transition points leading to degradation
in downstream planning performance. Our key insight is that instead of
predicting the most likely human motion, it is sufficient to produce forecasts
that capture how future human motion would affect the cost of a robot's plan.
We present ManiCast, a novel framework that learns cost-aware human forecasts
and feeds them to a model predictive control planner to execute collaborative
manipulation tasks. Our framework enables fluid, real-time interactions between
a human and a 7-DoF robot arm across a number of real-world tasks such as
reactive stirring, object handovers, and collaborative table setting. We
evaluate both the motion forecasts and the end-to-end forecaster-planner system
against a range of learned and heuristic baselines while additionally
contributing new datasets. We release our code and datasets at
https://portal-cornell.github.io/manicast/. | [
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] |
We propose a neural network-based meta-learning method to efficiently solve
partial differential equation (PDE) problems. The proposed method is designed
to meta-learn how to solve a wide variety of PDE problems, and uses the
knowledge for solving newly given PDE problems. We encode a PDE problem into a
problem representation using neural networks, where governing equations are
represented by coefficients of a polynomial function of partial derivatives,
and boundary conditions are represented by a set of point-condition pairs. We
use the problem representation as an input of a neural network for predicting
solutions, which enables us to efficiently predict problem-specific solutions
by the forwarding process of the neural network without updating model
parameters. To train our model, we minimize the expected error when adapted to
a PDE problem based on the physics-informed neural network framework, by which
we can evaluate the error even when solutions are unknown. We demonstrate that
our proposed method outperforms existing methods in predicting solutions of PDE
problems. | [
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] |
Backpropagation within neural networks leverages a fundamental element of
automatic differentiation, which is referred to as the reverse mode
differentiation, or vector Jacobian Product (VJP) or, in the context of
differential geometry, known as the pull-back process. The computation of
gradient is important as update of neural network parameters is performed using
gradient descent method. In this study, we present a genric randomized method,
which updates the parameters of neural networks by using directional
derivatives of loss functions computed efficiently by using forward mode AD or
Jacobian vector Product (JVP). These JVP are computed along the random
directions sampled from different probability distributions e.g., Bernoulli,
Normal, Wigner, Laplace and Uniform distributions. The computation of gradient
is performed during the forward pass of the neural network. We also present a
rigorous analysis of the presented methods providing the rate of convergence
along with the computational experiments deployed in scientific Machine
learning in particular physics-informed neural networks and Deep Operator
Networks. | [
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] |
Simply-verifiable mathematical conditions for existence, uniqueness and
explicit analytical computation of minimal adversarial paths (MAP) and minimal
adversarial distances (MAD) for (locally) uniquely-invertible classifiers, for
generalized linear models (GLM), and for entropic AI (EAI) are formulated and
proven. Practical computation of MAP and MAD, their comparison and
interpretations for various classes of AI tools (for neuronal networks, boosted
random forests, GLM and EAI) are demonstrated on the common synthetic
benchmarks: on a double Swiss roll spiral and its extensions, as well as on the
two biomedical data problems (for the health insurance claim predictions, and
for the heart attack lethality classification). On biomedical applications it
is demonstrated how MAP provides unique minimal patient-specific
risk-mitigating interventions in the predefined subsets of accessible control
variables. | [
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High dimension, low sample size (HDLSS) problems are numerous among
real-world applications of machine learning. From medical images to text
processing, traditional machine learning algorithms are usually unsuccessful in
learning the best possible concept from such data. In a previous work, we
proposed a dissimilarity-based approach for multi-view classification, the
Random Forest Dissimilarity (RFD), that perfoms state-of-the-art results for
such problems. In this work, we transpose the core principle of this approach
to solving HDLSS classification problems, by using the RF similarity measure as
a learned precomputed SVM kernel (RFSVM). We show that such a learned
similarity measure is particularly suited and accurate for this classification
context. Experiments conducted on 40 public HDLSS classification datasets,
supported by rigorous statistical analyses, show that the RFSVM method
outperforms existing methods for the majority of HDLSS problems and remains at
the same time very competitive for low or non-HDLSS problems. | [
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] |
This paper tackles the problem of the worst-class error rate, instead of the
standard error rate averaged over all classes. For example, a three-class
classification task with class-wise error rates of 10\%, 10\%, and 40\% has a
worst-class error rate of 40\%, whereas the average is 20\% under the
class-balanced condition. The worst-class error is important in many
applications. For example, in a medical image classification task, it would not
be acceptable for the malignant tumor class to have a 40\% error rate, while
the benign and healthy classes have 10\% error rates.We propose a boosting
algorithm that guarantees an upper bound of the worst-class training error and
derive its generalization bound. Experimental results show that the algorithm
lowers worst-class test error rates while avoiding overfitting to the training
set. | [
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] |
Diffusion model has become a main paradigm for synthetic data generation in
many subfields of modern machine learning, including computer vision, language
model, or speech synthesis. In this paper, we leverage the power of diffusion
model for generating synthetic tabular data. The heterogeneous features in
tabular data have been main obstacles in tabular data synthesis, and we tackle
this problem by employing the auto-encoder architecture. When compared with the
state-of-the-art tabular synthesizers, the resulting synthetic tables from our
model show nice statistical fidelities to the real data, and perform well in
downstream tasks for machine learning utilities. We conducted the experiments
over 15 publicly available datasets. Notably, our model adeptly captures the
correlations among features, which has been a long-standing challenge in
tabular data synthesis. Our code is available upon request and will be publicly
released if paper is accepted. | [
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0.0517372265458107,
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] |
Convection initiation (CI) nowcasting remains a challenging problem for both
numerical weather prediction models and existing nowcasting algorithms. In this
study, object-based probabilistic deep learning models are developed to predict
CI based on multichannel infrared GOES-R satellite observations. The data come
from patches surrounding potential CI events identified in Multi-Radar
Multi-Sensor Doppler weather radar products over the Great Plains region from
June and July 2020 and June 2021. An objective radar-based approach is used to
identify these events. The deep learning models significantly outperform the
classical logistic model at lead times up to 1 hour, especially on the false
alarm ratio. Through case studies, the deep learning model exhibits the
dependence on the characteristics of clouds and moisture at multiple levels.
Model explanation further reveals the model's decision-making process with
different baselines. The explanation results highlight the importance of
moisture and cloud features at different levels depending on the choice of
baseline. Our study demonstrates the advantage of using different baselines in
further understanding model behavior and gaining scientific insights. | [
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] |
Path reasoning methods over knowledge graphs have gained popularity for their
potential to improve transparency in recommender systems. However, the
resulting models still rely on pre-trained knowledge graph embeddings, fail to
fully exploit the interdependence between entities and relations in the KG for
recommendation, and may generate inaccurate explanations. In this paper, we
introduce PEARLM, a novel approach that efficiently captures user behaviour and
product-side knowledge through language modelling. With our approach, knowledge
graph embeddings are directly learned from paths over the KG by the language
model, which also unifies entities and relations in the same optimisation
space. Constraints on the sequence decoding additionally guarantee path
faithfulness with respect to the KG. Experiments on two datasets show the
effectiveness of our approach compared to state-of-the-art baselines. Source
code and datasets: AVAILABLE AFTER GETTING ACCEPTED. | [
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] |
A common approach to evaluating the significance of a collection of
$p$-values combines them with a pooling function, in particular when the
original data are not available. These pooled $p$-values convert a sample of
$p$-values into a single number which behaves like a univariate $p$-value. To
clarify discussion of these functions, a telescoping series of alternative
hypotheses are introduced that communicate the strength and prevalence of
non-null evidence in the $p$-values before general pooling formulae are
discussed. A pattern noticed in the UMP pooled $p$-value for a particular
alternative motivates the definition and discussion of central and marginal
rejection levels at $\alpha$. It is proven that central rejection is always
greater than or equal to marginal rejection, motivating a quotient to measure
the balance between the two for pooled $p$-values. A combining function based
on the $\chi^2_{\kappa}$ quantile transformation is proposed to control this
quotient and shown to be robust to mis-specified parameters relative to the
UMP. Different powers for different parameter settings motivate a map of
plausible alternatives based on where this pooled $p$-value is minimized. | [
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] |
To process sensor data in the Internet of Things(IoTs), embedded deep
learning for 1-dimensional data is an important technique. In the past, CNNs
were frequently used because they are simple to optimise for special embedded
hardware such as FPGAs. This work proposes a novel LSTM cell optimisation aimed
at energy-efficient inference on end devices. Using the traffic speed
prediction as a case study, a vanilla LSTM model with the optimised LSTM cell
achieves 17534 inferences per second while consuming only 3.8 $\mu$J per
inference on the FPGA \textit{XC7S15} from \textit{Spartan-7} family. It
achieves at least 5.4$\times$ faster throughput and 1.37$\times$ more energy
efficient than existing approaches. | [
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] |
The Motion Manifold Primitive (MMP) produces, for a given task, a continuous
manifold of trajectories each of which can successfully complete the task. It
consists of the decoder function that parametrizes the manifold and the
probability density in the latent coordinate space. In this paper, we first
show that the MMP performance can significantly degrade due to the geometric
distortion in the latent space -- by distortion, we mean that similar motions
are not located nearby in the latent space. We then propose {\it Isometric
Motion Manifold Primitives (IMMP)} whose latent coordinate space preserves the
geometry of the manifold. For this purpose, we formulate and use a Riemannian
metric for the motion space (i.e., parametric curve space), which we call a
{\it CurveGeom Riemannian metric}. Experiments with planar obstacle-avoiding
motions and pushing manipulation tasks show that IMMP significantly outperforms
existing MMP methods. Code is available at
https://github.com/Gabe-YHLee/IMMP-public. | [
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] |
There have recently been significant advances in the problem of unsupervised
object-centric representation learning and its application to downstream tasks.
The latest works support the argument that employing disentangled object
representations in image-based object-centric reinforcement learning tasks
facilitates policy learning. We propose a novel object-centric reinforcement
learning algorithm combining actor-critic and model-based approaches to utilize
these representations effectively. In our approach, we use a transformer
encoder to extract object representations and graph neural networks to
approximate the dynamics of an environment. The proposed method fills a
research gap in developing efficient object-centric world models for
reinforcement learning settings that can be used for environments with discrete
or continuous action spaces. Our algorithm performs better in a visually
complex 3D robotic environment and a 2D environment with compositional
structure than the state-of-the-art model-free actor-critic algorithm built
upon transformer architecture and the state-of-the-art monolithic model-based
algorithm. | [
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] |
The efficient deployment and fine-tuning of foundation models are pivotal in
contemporary artificial intelligence. In this study, we present a
groundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundation
models, specifically designed to enhance local task performance on user
equipment (UE). Central to our approach is the innovative Emulator-Adapter
architecture, segmenting the foundation model into two cohesive modules. This
design not only conserves computational resources but also ensures adaptability
and fine-tuning efficiency for downstream tasks. Additionally, we introduce an
advanced resource allocation mechanism that is fine-tuned to the needs of the
Emulator-Adapter structure in decentralized settings. To address the challenges
presented by this system, we employ a hybrid multi-agent Deep Reinforcement
Learning (DRL) strategy, adept at handling mixed discrete-continuous action
spaces, ensuring dynamic and optimal resource allocations. Our comprehensive
simulations and validations underscore the practical viability of our approach,
demonstrating its robustness, efficiency, and scalability. Collectively, this
work offers a fresh perspective on deploying foundation models and balancing
computational efficiency with task proficiency. | [
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] |
The ability to learn and refine behavior after deployment has become ever
more important for robots as we design them to operate in unstructured
environments like households. In this work, we design a new learning system
based on large language model (LLM), OLAF, that allows everyday users to teach
a robot using verbal corrections when the robot makes mistakes, e.g., by saying
"Stop what you're doing. You should move closer to the cup." A key feature of
OLAF is its ability to update the robot's visuomotor neural policy based on the
verbal feedback to avoid repeating mistakes in the future. This is in contrast
to existing LLM-based robotic systems, which only follow verbal commands or
corrections but not learn from them. We demonstrate the efficacy of our design
in experiments where a user teaches a robot to perform long-horizon
manipulation tasks both in simulation and on physical hardware, achieving on
average 20.0% improvement in policy success rate. Videos and more results are
at https://ut-austin-rpl.github.io/olaf/ | [
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] |
Deep reinforcement learning (RL) can enable robots to autonomously acquire
complex behaviors, such as legged locomotion. However, RL in the real world is
complicated by constraints on efficiency, safety, and overall training
stability, which limits its practical applicability. We present APRL, a policy
regularization framework that modulates the robot's exploration over the course
of training, striking a balance between flexible improvement potential and
focused, efficient exploration. APRL enables a quadrupedal robot to efficiently
learn to walk entirely in the real world within minutes and continue to improve
with more training where prior work saturates in performance. We demonstrate
that continued training with APRL results in a policy that is substantially
more capable of navigating challenging situations and is able to adapt to
changes in dynamics with continued training. | [
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] |
The use of large language models for code generation is a rapidly growing
trend in software development. However, without effective methods for ensuring
the correctness of generated code, this trend could lead to any number of
undesirable outcomes. In this paper, we lay out a vision for addressing this
challenge: the Clover paradigm, short for Closed-Loop Verifiable Code
Generation, which reduces correctness checking to the more accessible problem
of consistency checking. At the core of Clover lies a checker that performs
consistency checks among code, docstrings, and formal annotations. The checker
is implemented using a novel integration of formal verification tools and large
language models. We provide a theoretical analysis to support our thesis that
Clover should be effective at consistency checking. We also empirically
investigate its feasibility on a hand-designed dataset (CloverBench) featuring
annotated Dafny programs at a textbook level of difficulty. Experimental
results show that for this dataset, (i) LLMs are reasonably successful at
automatically generating formal specifications; and (ii) our consistency
checker achieves a promising acceptance rate (up to 87%) for correct instances
while maintaining zero tolerance for incorrect ones (no false positives). | [
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] |
Bayesian inference with deep generative prior has received considerable
interest for solving imaging inverse problems in many scientific and
engineering fields. The selection of the prior distribution is learned from,
and therefore an important representation learning of, available prior
measurements. The SA-Roundtrip, a novel deep generative prior, is introduced to
enable controlled sampling generation and identify the data's intrinsic
dimension. This prior incorporates a self-attention structure within a
bidirectional generative adversarial network. Subsequently, Bayesian inference
is applied to the posterior distribution in the low-dimensional latent space
using the Hamiltonian Monte Carlo with preconditioned Crank-Nicolson (HMC-pCN)
algorithm, which is proven to be ergodic under specific conditions. Experiments
conducted on computed tomography (CT) reconstruction with the MNIST and
TomoPhantom datasets reveal that the proposed method outperforms
state-of-the-art comparisons, consistently yielding a robust and superior point
estimator along with precise uncertainty quantification. | [
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] |
We introduce the problem of ranking with slot constraints, which can be used
to model a wide range of application problems -- from college admission with
limited slots for different majors, to composing a stratified cohort of
eligible participants in a medical trial. We show that the conventional
Probability Ranking Principle (PRP) can be highly sub-optimal for
slot-constrained ranking problems, and we devise a new ranking algorithm,
called MatchRank. The goal of MatchRank is to produce rankings that maximize
the number of filled slots if candidates are evaluated by a human decision
maker in the order of the ranking. In this way, MatchRank generalizes the PRP,
and it subsumes the PRP as a special case when there are no slot constraints.
Our theoretical analysis shows that MatchRank has a strong approximation
guarantee without any independence assumptions between slots or candidates.
Furthermore, we show how MatchRank can be implemented efficiently. Beyond the
theoretical guarantees, empirical evaluations show that MatchRank can provide
substantial improvements over a range of synthetic and real-world tasks. | [
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] |
Thanks to the outstanding achievements from state-of-the-art generative
models like ChatGPT and diffusion models, generative AI has gained substantial
attention across various industrial and academic domains. In this paper,
denoising diffusion probabilistic models (DDPMs) are proposed for a practical
finite-precision wireless communication system with hardware-impaired
transceivers. The intuition behind DDPM is to decompose the data generation
process over the so-called "denoising" steps. Inspired by this, a DDPM-based
receiver is proposed for a practical wireless communication scheme that faces
realistic non-idealities, including hardware impairments (HWI), channel
distortions, and quantization errors. It is shown that our approach provides
network resilience under low-SNR regimes, near-invariant reconstruction
performance with respect to different HWI levels and quantization errors, and
robust out-of-distribution performance against non-Gaussian noise. Moreover,
the reconstruction performance of our scheme is evaluated in terms of cosine
similarity and mean-squared error (MSE), highlighting more than 25 dB
improvement compared to the conventional deep neural network (DNN)-based
receivers. | [
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] |
In this paper, we prove the first Bayesian regret bounds for Thompson
Sampling in reinforcement learning in a multitude of settings. We simplify the
learning problem using a discrete set of surrogate environments, and present a
refined analysis of the information ratio using posterior consistency. This
leads to an upper bound of order $\widetilde{O}(H\sqrt{d_{l_1}T})$ in the time
inhomogeneous reinforcement learning problem where $H$ is the episode length
and $d_{l_1}$ is the Kolmogorov $l_1-$dimension of the space of environments.
We then find concrete bounds of $d_{l_1}$ in a variety of settings, such as
tabular, linear and finite mixtures, and discuss how how our results are either
the first of their kind or improve the state-of-the-art. | [
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] |
Understanding the ubiquitous phenomenon of neural synchronization across
species and organizational levels is crucial for decoding brain function.
Despite its prevalence, the specific functional role, origin, and dynamical
implication of modular structures in correlation-based networks remains
ambiguous. Using recurrent neural networks trained on systems neuroscience
tasks, this study investigates these important characteristics of modularity in
correlation networks. We demonstrate that modules are functionally coherent
units that contribute to specialized information processing. We show that
modules form spontaneously from asymmetries in the sign and weight of
projections from the input layer to the recurrent layer. Moreover, we show that
modules define connections with similar roles in governing system behavior and
dynamics. Collectively, our findings clarify the function, formation, and
operational significance of functional connectivity modules, offering insights
into cortical function and laying the groundwork for further studies on brain
function, development, and dynamics. | [
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] |
Using backward error analysis, we compute implicit training biases in
multitask and continual learning settings for neural networks trained with
stochastic gradient descent. In particular, we derive modified losses that are
implicitly minimized during training. They have three terms: the original loss,
accounting for convergence, an implicit flatness regularization term
proportional to the learning rate, and a last term, the conflict term, which
can theoretically be detrimental to both convergence and implicit
regularization. In multitask, the conflict term is a well-known quantity,
measuring the gradient alignment between the tasks, while in continual learning
the conflict term is a new quantity in deep learning optimization, although a
basic tool in differential geometry: The Lie bracket between the task
gradients. | [
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] |
We consider the problem of estimating the counterfactual joint distribution
of multiple quantities of interests (e.g., outcomes) in a multivariate causal
model extended from the classical difference-in-difference design. Existing
methods for this task either ignore the correlation structures among dimensions
of the multivariate outcome by considering univariate causal models on each
dimension separately and hence produce incorrect counterfactual distributions,
or poorly scale even for moderate-size datasets when directly dealing with such
multivariate causal model. We propose a method that alleviates both issues
simultaneously by leveraging a robust latent one-dimensional subspace of the
original high-dimension space and exploiting the efficient estimation from the
univariate causal model on such space. Since the construction of the
one-dimensional subspace uses information from all the dimensions, our method
can capture the correlation structures and produce good estimates of the
counterfactual distribution. We demonstrate the advantages of our approach over
existing methods on both synthetic and real-world data. | [
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Model extraction is a growing concern for the security of AI systems. For
deep neural network models, the architecture is the most important information
an adversary aims to recover. Being a sequence of repeated computation blocks,
neural network models deployed on edge-devices will generate distinctive
side-channel leakages. The latter can be exploited to extract critical
information when targeted platforms are physically accessible. By combining
theoretical knowledge about deep learning practices and analysis of a
widespread implementation library (ARM CMSIS-NN), our purpose is to answer this
critical question: how far can we extract architecture information by simply
examining an EM side-channel trace? For the first time, we propose an
extraction methodology for traditional MLP and CNN models running on a high-end
32-bit microcontroller (Cortex-M7) that relies only on simple pattern
recognition analysis. Despite few challenging cases, we claim that, contrary to
parameters extraction, the complexity of the attack is relatively low and we
highlight the urgent need for practicable protections that could fit the strong
memory and latency requirements of such platforms. | [
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] |
Neural networks have shown their effectiveness in various tasks in the realm
of quantum computing. However, their application in quantum error mitigation, a
crucial step towards realizing practical quantum advancements, has been
restricted by reliance on noise-free statistics. To tackle this critical
challenge, we propose a data augmentation empowered neural model for error
mitigation (DAEM). Our model does not require any prior knowledge about the
specific noise type and measurement settings and can estimate noise-free
statistics solely from the noisy measurement results of the target quantum
process, rendering it highly suitable for practical implementation. In
numerical experiments, we show the model's superior performance in mitigating
various types of noise, including Markovian noise and Non-Markovian noise,
compared with previous error mitigation methods. We further demonstrate its
versatility by employing the model to mitigate errors in diverse types of
quantum processes, including those involving large-scale quantum systems and
continuous-variable quantum states. This powerful data augmentation-empowered
neural model for error mitigation establishes a solid foundation for realizing
more reliable and robust quantum technologies in practical applications. | [
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] |
Model explainability is crucial for human users to be able to interpret how a
proposed classifier assigns labels to data based on its feature values. We
study generalized linear models constructed using sets of feature value rules,
which can capture nonlinear dependencies and interactions. An inherent
trade-off exists between rule set sparsity and its prediction accuracy. It is
computationally expensive to find the right choice of sparsity -- e.g., via
cross-validation -- with existing methods. We propose a new formulation to
learn an ensemble of rule sets that simultaneously addresses these competing
factors. Good generalization is ensured while keeping computational costs low
by utilizing distributionally robust optimization. The formulation utilizes
column generation to efficiently search the space of rule sets and constructs a
sparse ensemble of rule sets, in contrast with techniques like random forests
or boosting and their variants. We present theoretical results that motivate
and justify the use of our distributionally robust formulation. Extensive
numerical experiments establish that our method improves over competing methods
-- on a large set of publicly available binary classification problem instances
-- with respect to one or more of the following metrics: generalization
quality, computational cost, and explainability. | [
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] |
Quantum computing has recently emerged as a transformative technology. Yet,
its promised advantages rely on efficiently translating quantum operations into
viable physical realizations. In this work, we use generative machine learning
models, specifically denoising diffusion models (DMs), to facilitate this
transformation. Leveraging text-conditioning, we steer the model to produce
desired quantum operations within gate-based quantum circuits. Notably, DMs
allow to sidestep during training the exponential overhead inherent in the
classical simulation of quantum dynamics -- a consistent bottleneck in
preceding ML techniques. We demonstrate the model's capabilities across two
tasks: entanglement generation and unitary compilation. The model excels at
generating new circuits and supports typical DM extensions such as masking and
editing to, for instance, align the circuit generation to the constraints of
the targeted quantum device. Given their flexibility and generalization
abilities, we envision DMs as pivotal in quantum circuit synthesis, enhancing
both practical applications but also insights into theoretical quantum
computation. | [
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] |
While molecular pre-training has shown great potential in enhancing drug
discovery, the lack of a solid physical interpretation in current methods
raises concerns about whether the learned representation truly captures the
underlying explanatory factors in observed data, ultimately resulting in
limited generalization and robustness. Although denoising methods offer a
physical interpretation, their accuracy is often compromised by ad-hoc noise
design, leading to inaccurate learned force fields. To address this limitation,
this paper proposes a new method for molecular pre-training, called sliced
denoising (SliDe), which is based on the classical mechanical intramolecular
potential theory. SliDe utilizes a novel noise strategy that perturbs bond
lengths, angles, and torsion angles to achieve better sampling over
conformations. Additionally, it introduces a random slicing approach that
circumvents the computationally expensive calculation of the Jacobian matrix,
which is otherwise essential for estimating the force field. By aligning with
physical principles, SliDe shows a 42\% improvement in the accuracy of
estimated force fields compared to current state-of-the-art denoising methods,
and thus outperforms traditional baselines on various molecular property
prediction tasks. | [
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] |
Reinforcement Learning (RL) plays an important role in the robotic
manipulation domain since it allows self-learning from trial-and-error
interactions with the environment. Still, sample efficiency and reward
specification seriously limit its potential. One possible solution involves
learning from expert guidance. However, obtaining a human expert is impractical
due to the high cost of supervising an RL agent, and developing an automatic
supervisor is a challenging endeavor. Large Language Models (LLMs) demonstrate
remarkable abilities to provide human-like feedback on user inputs in natural
language. Nevertheless, they are not designed to directly control low-level
robotic motions, as their pretraining is based on vast internet data rather
than specific robotics data. In this paper, we introduce the Lafite-RL
(Language agent feedback interactive Reinforcement Learning) framework, which
enables RL agents to learn robotic tasks efficiently by taking advantage of
LLMs' timely feedback. Our experiments conducted on RLBench tasks illustrate
that, with simple prompt design in natural language, the Lafite-RL agent
exhibits improved learning capabilities when guided by an LLM. It outperforms
the baseline in terms of both learning efficiency and success rate,
underscoring the efficacy of the rewards provided by an LLM. | [
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] |
Spoken language understanding systems using audio-only data are gaining
popularity, yet their ability to handle unseen intents remains limited. In this
study, we propose a generalized zero-shot audio-to-intent classification
framework with only a few sample text sentences per intent. To achieve this, we
first train a supervised audio-to-intent classifier by making use of a
self-supervised pre-trained model. We then leverage a neural audio synthesizer
to create audio embeddings for sample text utterances and perform generalized
zero-shot classification on unseen intents using cosine similarity. We also
propose a multimodal training strategy that incorporates lexical information
into the audio representation to improve zero-shot performance. Our multimodal
training approach improves the accuracy of zero-shot intent classification on
unseen intents of SLURP by 2.75% and 18.2% for the SLURP and internal
goal-oriented dialog datasets, respectively, compared to audio-only training. | [
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] |
Despite the breakthroughs in biomarker discovery facilitated by differential
gene analysis, challenges remain, particularly at the single-cell level.
Traditional methodologies heavily rely on user-supplied cell annotations,
focusing on individually expressed data, often neglecting the critical
interactions between biological conditions, such as healthy versus diseased
states. In response, here we introduce scBeacon, an innovative framework built
upon a deep contrastive siamese network. scBeacon pioneers an unsupervised
approach, adeptly identifying matched cell populations across varied
conditions, enabling a refined differential gene analysis. By utilizing a
VQ-VAE framework, a contrastive siamese network, and a greedy iterative
strategy, scBeacon effectively pinpoints differential genes that hold potential
as key biomarkers. Comprehensive evaluations on a diverse array of datasets
validate scBeacon's superiority over existing single-cell differential gene
analysis tools. Its precision and adaptability underscore its significant role
in enhancing diagnostic accuracy in biomarker discovery. With the emphasis on
the importance of biomarkers in diagnosis, scBeacon is positioned to be a
pivotal asset in the evolution of personalized medicine and targeted
treatments. | [
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] |
Explainable AI (XAI) has been proposed as a valuable tool to assist in
downstream tasks involving human and AI collaboration. Perhaps the most
psychologically valid XAI techniques are case based approaches which display
'whole' exemplars to explain the predictions of black box AI systems. However,
for such post hoc XAI methods dealing with images, there has been no attempt to
improve their scope by using multiple clear feature 'parts' of the images to
explain the predictions while linking back to relevant cases in the training
data, thus allowing for more comprehensive explanations that are faithful to
the underlying model. Here, we address this gap by proposing two general
algorithms (latent and super pixel based) which can isolate multiple clear
feature parts in a test image, and then connect them to the explanatory cases
found in the training data, before testing their effectiveness in a carefully
designed user study. Results demonstrate that the proposed approach
appropriately calibrates a users feelings of 'correctness' for ambiguous
classifications in real world data on the ImageNet dataset, an effect which
does not happen when just showing the explanation without feature highlighting. | [
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] |
Humans demonstrate an impressive ability to acquire and generalize
manipulation "tricks." Even from a single demonstration, such as using soup
ladles to reach for distant objects, we can apply this skill to new scenarios
involving different object positions, sizes, and categories (e.g., forks and
hammers). Additionally, we can flexibly combine various skills to devise
long-term plans. In this paper, we present a framework that enables machines to
acquire such manipulation skills, referred to as "mechanisms," through a single
demonstration and self-play. Our key insight lies in interpreting each
demonstration as a sequence of changes in robot-object and object-object
contact modes, which provides a scaffold for learning detailed samplers for
continuous parameters. These learned mechanisms and samplers can be seamlessly
integrated into standard task and motion planners, enabling their compositional
use. | [
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] |
Recommender models aim to capture user preferences from historical feedback
and then predict user-specific feedback on candidate items. However, the
presence of various unmeasured confounders causes deviations between the user
preferences in the historical feedback and the true preferences, resulting in
models not meeting their expected performance. Existing debias models either
(1) specific to solving one particular bias or (2) directly obtain auxiliary
information from user historical feedback, which cannot identify whether the
learned preferences are true user preferences or mixed with unmeasured
confounders. Moreover, we find that the former recommender system is not only a
successor to unmeasured confounders but also acts as an unmeasured confounder
affecting user preference modeling, which has always been neglected in previous
studies. To this end, we incorporate the effect of the former recommender
system and treat it as a proxy for all unmeasured confounders. We propose a
novel framework, \textbf{S}eparating and \textbf{L}earning Latent Confounders
\textbf{F}or \textbf{R}ecommendation (\textbf{SLFR}), which obtains the
representation of unmeasured confounders to identify the counterfactual
feedback by disentangling user preferences and unmeasured confounders, then
guides the target model to capture the true preferences of users. Extensive
experiments in five real-world datasets validate the advantages of our method. | [
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] |
Synthetic biologists and molecular programmers design novel nucleic acid
reactions, with many potential applications. Good visualization tools are
needed to help domain experts make sense of the complex outputs of folding
pathway simulations of such reactions. Here we present ViDa, a new approach for
visualizing DNA reaction folding trajectories over the energy landscape of
secondary structures. We integrate a deep graph embedding model with common
dimensionality reduction approaches, to map high-dimensional data onto 2D
Euclidean space. We assess ViDa on two well-studied and contrasting DNA
hybridization reactions. Our preliminary results suggest that ViDa's
visualization successfully separates trajectories with different folding
mechanisms, thereby providing useful insight to users, and is a big improvement
over the current state-of-the-art in DNA kinetics visualization. | [
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] |
Clinical variant classification of pathogenic versus benign genetic variants
remains a pivotal challenge in clinical genetics. Recently, the proposition of
protein language models has improved the generic variant effect prediction
(VEP) accuracy via weakly-supervised or unsupervised training. However, these
VEPs are not disease-specific, limiting their adaptation at point-of-care. To
address this problem, we propose a disease-specific \textsc{pro}tein language
model for variant \textsc{path}ogenicity, termed ProPath, to capture the
pseudo-log-likelihood ratio in rare missense variants through a siamese
network. We evaluate the performance of ProPath against pre-trained language
models, using clinical variant sets in inherited cardiomyopathies and
arrhythmias that were not seen during training. Our results demonstrate that
ProPath surpasses the pre-trained ESM1b with an over $5\%$ improvement in AUC
across both datasets. Furthermore, our model achieved the highest performances
across all baselines for both datasets. Thus, our ProPath offers a potent
disease-specific variant effect prediction, particularly valuable for disease
associations and clinical applicability. | [
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] |
While recommender systems have become an integral component of the Web
experience, their heavy reliance on user data raises privacy and security
concerns. Substituting user data with synthetic data can address these
concerns, but accurately replicating these real-world datasets has been a
notoriously challenging problem. Recent advancements in generative AI have
demonstrated the impressive capabilities of diffusion models in generating
realistic data across various domains. In this work we introduce a Score-based
Diffusion Recommendation Model (SDRM), which captures the intricate patterns of
real-world datasets required for training highly accurate recommender systems.
SDRM allows for the generation of synthetic data that can replace existing
datasets to preserve user privacy, or augment existing datasets to address
excessive data sparsity. Our method outperforms competing baselines such as
generative adversarial networks, variational autoencoders, and recently
proposed diffusion models in synthesizing various datasets to replace or
augment the original data by an average improvement of 4.30% in Recall@$n$ and
4.65% in NDCG@$n$. | [
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In the modern era, malware is experiencing a significant increase in both its
variety and quantity, aligning with the widespread adoption of the digital
world. This surge in malware has emerged as a critical challenge in the realm
of cybersecurity, prompting numerous research endeavors and contributions to
address the issue. Machine learning algorithms have been leveraged for malware
detection due to their ability to uncover concealed patterns within vast
datasets. However, deep learning algorithms, characterized by their
multi-layered structure, surpass the limitations of traditional machine
learning approaches. By employing deep learning techniques such as CNN
(Convolutional Neural Network) and RNN (Recurrent Neural Network), this study
aims to classify and identify malware extracted from a dataset containing API
call sequences. The performance of these algorithms is compared with that of
conventional machine learning methods, including SVM (Support Vector Machine),
RF (Random Forest), KNN (K-Nearest Neighbors), XGB (Extreme Gradient Boosting),
and GBC (Gradient Boosting Classifier), all using the same dataset. The
outcomes of this research demonstrate that both deep learning and machine
learning algorithms achieve remarkably high levels of accuracy, reaching up to
99% in certain cases. | [
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] |
Quantum tangent kernel methods provide an efficient approach to analyzing the
performance of quantum machine learning models in the infinite-width limit,
which is of crucial importance in designing appropriate circuit architectures
for certain learning tasks. Recently, they have been adapted to describe the
convergence rate of training errors in quantum neural networks in an analytical
manner. Here, we study the connections between the trainability and
expressibility of quantum tangent kernel models. In particular, for global loss
functions, we rigorously prove that high expressibility of both the global and
local quantum encodings can lead to exponential concentration of quantum
tangent kernel values to zero. Whereas for local loss functions, such issue of
exponential concentration persists owing to the high expressibility, but can be
partially mitigated. We further carry out extensive numerical simulations to
support our analytical theories. Our discoveries unveil a pivotal
characteristic of quantum neural tangent kernels, offering valuable insights
for the design of wide quantum variational circuit models in practical
applications. | [
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] |
Apprenticeship learning crucially depends on effectively learning rewards,
and hence control policies from user demonstrations. Of particular difficulty
is the setting where the desired task consists of a number of sub-goals with
temporal dependencies. The quality of inferred rewards and hence policies are
typically limited by the quality of demonstrations, and poor inference of these
can lead to undesirable outcomes. In this letter, we show how temporal logic
specifications that describe high level task objectives, are encoded in a graph
to define a temporal-based metric that reasons about behaviors of demonstrators
and the learner agent to improve the quality of inferred rewards and policies.
Through experiments on a diverse set of robot manipulator simulations, we show
how our framework overcomes the drawbacks of prior literature by drastically
improving the number of demonstrations required to learn a control policy. | [
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] |
Bitcoin as a cryptocurrency has been one of the most important digital coins
and the first decentralized digital currency. Deep neural networks, on the
other hand, has shown promising results recently; however, we require huge
amount of high-quality data to leverage their power. There are some techniques
such as augmentation that can help us with increasing the dataset size, but we
cannot exploit them on historical bitcoin data. As a result, we propose a
shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data
using our proposed method to forecast bitcoin closing prices in a daily time
frame. We compare the performance with that of other forecasting methods, and
show that with the help of the proposed feature engineering method, a shallow
deep neural network outperforms other popular price forecasting models. | [
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] |
The Human Activity Recognition (HAR) tasks automatically identify human
activities using the sensor data, which has numerous applications in
healthcare, sports, security, and human-computer interaction. Despite
significant advances in HAR, critical challenges still exist. Game theory has
emerged as a promising solution to address these challenges in machine learning
problems including HAR. However, there is a lack of research work on applying
game theory solutions to the HAR problems. This review paper explores the
potential of game theory as a solution for HAR tasks, and bridges the gap
between game theory and HAR research work by suggesting novel game-theoretic
approaches for HAR problems. The contributions of this work include exploring
how game theory can improve the accuracy and robustness of HAR models,
investigating how game-theoretic concepts can optimize recognition algorithms,
and discussing the game-theoretic approaches against the existing HAR methods.
The objective is to provide insights into the potential of game theory as a
solution for sensor-based HAR, and contribute to develop a more accurate and
efficient recognition system in the future research directions. | [
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] |
The Recommender system is a vital information service on today's Internet.
Recently, graph neural networks have emerged as the leading approach for
recommender systems. We try to review recent literature on graph neural
network-based recommender systems, covering the background and development of
both recommender systems and graph neural networks. Then categorizing
recommender systems by their settings and graph neural networks by spectral and
spatial models, we explore the motivation behind incorporating graph neural
networks into recommender systems. We also analyze challenges and open problems
in graph construction, embedding propagation and aggregation, and computation
efficiency. This guides us to better explore the future directions and
developments in this domain. | [
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Causal discovery is becoming a key part in medical AI research. These methods
can enhance healthcare by identifying causal links between biomarkers,
demographics, treatments and outcomes. They can aid medical professionals in
choosing more impactful treatments and strategies. In parallel, Large Language
Models (LLMs) have shown great potential in identifying patterns and generating
insights from text data. In this paper we investigate applying LLMs to the
problem of determining the directionality of edges in causal discovery.
Specifically, we test our approach on a deidentified set of Non Small Cell Lung
Cancer(NSCLC) patients that have both electronic health record and genomic
panel data. Graphs are validated using Bayesian Dirichlet estimators using
tabular data. Our result shows that LLMs can accurately predict the
directionality of edges in causal graphs, outperforming existing
state-of-the-art methods. These findings suggests that LLMs can play a
significant role in advancing causal discovery and help us better understand
complex systems. | [
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] |
This study addresses a gap in the utilization of Reinforcement Learning (RL)
and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing
Problem (SVRP) that involves the challenging task of optimizing vehicle routes
under uncertain conditions. We propose a novel end-to-end framework that
comprehensively addresses the key sources of stochasticity in SVRP and utilizes
an RL agent with a simple yet effective architecture and a tailored training
method. Through comparative analysis, our proposed model demonstrates superior
performance compared to a widely adopted state-of-the-art metaheuristic,
achieving a significant 3.43% reduction in travel costs. Furthermore, the model
exhibits robustness across diverse SVRP settings, highlighting its adaptability
and ability to learn optimal routing strategies in varying environments. The
publicly available implementation of our framework serves as a valuable
resource for future research endeavors aimed at advancing RL-based solutions
for SVRP. | [
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] |
Slip and crumple detection is essential for performing robust manipulation
tasks with a robotic hand (RH) like remote surgery. It has been one of the
challenging problems in the robotics manipulation community. In this work, we
propose a technique based on machine learning (ML) based techniques to detect
the slip, and crumple as well as the shape of an object that is currently held
in the robotic hand. We proposed ML model will detect the slip, crumple, and
shape using the force/torque exerted and the angular positions of the actuators
present in the RH. The proposed model would be integrated into the loop of a
robotic hand(RH) and haptic glove(HG). This would help us to reduce the latency
in case of teleoperation | [
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] |
Orthogonal time frequency space (OTFS) is a promising modulation scheme for
wireless communication in high-mobility scenarios. Recently, a reservoir
computing (RC) based approach has been introduced for online subframe-based
symbol detection in the OTFS system, where only a limited number of
over-the-air (OTA) pilot symbols are utilized for training. However, this
approach does not leverage the domain knowledge specific to the OTFS system.
This paper introduces a novel two-dimensional RC (2D-RC) method that
incorporates the structural knowledge of the OTFS system into the design for
online symbol detection on a subframe basis. Specifically, as the channel
response acts as a two-dimensional (2D) operation over the transmitted
information symbols in the delay-Doppler (DD) domain, the 2D-RC is designed to
have a 2D structure to equalize the channel. With the introduced architecture,
the 2D-RC can benefit from the predictable channel representation in the DD
domain. Moreover, unlike the previous work that requires multiple RCs to learn
the channel feature, the 2D-RC only requires a single neural network for
detection. Experimental results demonstrate the effectiveness of the 2D-RC
approach across different OTFS system variants and modulation orders. | [
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] |
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which
offer possibilities for treating and alleviating diseases, with computational
methods can improve patient care and contribute to efficient drug development.
However, many existing computational methods require large amounts of known DDI
information, which is scarce for emerging drugs. In this paper, we propose
EmerGNN, a graph neural network (GNN) that can effectively predict interactions
for emerging drugs by leveraging the rich information in biomedical networks.
EmerGNN learns pairwise representations of drugs by extracting the paths
between drug pairs, propagating information from one drug to the other, and
incorporating the relevant biomedical concepts on the paths. The different
edges on the biomedical network are weighted to indicate the relevance for the
target DDI prediction. Overall, EmerGNN has higher accuracy than existing
approaches in predicting interactions for emerging drugs and can identify the
most relevant information on the biomedical network. | [
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] |
Research automation efforts usually employ AI as a tool to automate specific
tasks within the research process. To create an AI that truly conduct research
themselves, it must independently generate hypotheses, design verification
plans, and execute verification. Therefore, we investigated if an AI itself
could autonomously generate and verify hypothesis for a toy machine learning
research problem. We prompted GPT-4 to generate hypotheses and Python code for
hypothesis verification with limited methodological guidance. Our findings
suggest that, in some instances, GPT-4 can autonomously generate and validate
hypotheses without detailed guidance. While this is a promising result, we also
found that none of the verifications were flawless, and there remain
significant challenges in achieving autonomous, human-level research using only
generic instructions. These findings underscore the need for continued
exploration to develop a general and autonomous AI researcher. | [
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] |
In Computational Fluid Dynamics (CFD), coarse mesh simulations offer
computational efficiency but often lack precision. Applying conventional
super-resolution to these simulations poses a significant challenge due to the
fundamental contrast between downsampling high-resolution images and
authentically emulating low-resolution physics. The former method conserves
more of the underlying physics, surpassing the usual constraints of real-world
scenarios. We propose a novel definition of super-resolution tailored for
PDE-based problems. Instead of simply downsampling from a high-resolution
dataset, we use coarse-grid simulated data as our input and predict fine-grid
simulated outcomes. Employing a physics-infused UNet upscaling method, we
demonstrate its efficacy across various 2D-CFD problems such as discontinuity
detection in Burger's equation, Methane combustion, and fouling in Industrial
heat exchangers. Our method enables the generation of fine-mesh solutions
bypassing traditional simulation, ensuring considerable computational saving
and fidelity to the original ground truth outcomes. Through diverse boundary
conditions during training, we further establish the robustness of our method,
paving the way for its broad applications in engineering and scientific CFD
solvers. | [
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] |
Extensive research on formal verification of machine learning (ML) systems
indicates that learning from data alone often fails to capture underlying
background knowledge. A variety of verifiers have been developed to ensure that
a machine-learnt model satisfies correctness and safety properties, however,
these verifiers typically assume a trained network with fixed weights.
ML-enabled autonomous systems are required to not only detect incorrect
predictions, but should also possess the ability to self-correct, continuously
improving and adapting. A promising approach for creating ML models that
inherently satisfy constraints is to encode background knowledge as logical
constraints that guide the learning process via so-called differentiable
logics. In this research preview, we compare and evaluate various logics from
the literature in weakly-supervised contexts, presenting our findings and
highlighting open problems for future work. Our experimental results are
broadly consistent with results reported previously in literature; however,
learning with differentiable logics introduces a new hyperparameter that is
difficult to tune and has significant influence on the effectiveness of the
logics. | [
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It has been shown that a neural network model recently proposed to describe
basic memory performance is based on a ternary/binary coding/decoding algorithm
which leads to a new neural network assembly memory model (NNAMM) providing
maximum-likelihood recall/recognition properties and implying a new memory unit
architecture with Hopfield two-layer network, N-channel time gate, auxiliary
reference memory, and two nested feedback loops. For the data coding used,
conditions are found under which a version of Hopfied network implements
maximum-likelihood convolutional decoding algorithm and, simultaneously, linear
statistical classifier of arbitrary binary vectors with respect to Hamming
distance between vector analyzed and reference vector given. In addition to
basic memory performance and etc, the model explicitly describes the dependence
on time of memory trace retrieval, gives a possibility of one-trial learning,
metamemory simulation, generalized knowledge representation, and distinct
description of conscious and unconscious mental processes. It has been shown
that an assembly memory unit may be viewed as a model of a smallest inseparable
part or an 'atom' of consciousness. Some nontraditional neurobiological
backgrounds (dynamic spatiotemporal synchrony, properties of time dependent and
error detector neurons, early precise spike firing, etc) and the model's
application to solve some interdisciplinary problems from different scientific
fields are discussed. | [
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] |
A ternary/binary data coding algorithm and conditions under which Hopfield
networks implement optimal convolutional or Hamming decoding algorithms has
been described. Using the coding/decoding approach (an optimal Binary Signal
Detection Theory, BSDT) introduced a Neural Network Assembly Memory Model
(NNAMM) is built. The model provides optimal (the best) basic memory
performance and demands the use of a new memory unit architecture with
two-layer Hopfield network, N-channel time gate, auxiliary reference memory,
and two nested feedback loops. NNAMM explicitly describes the dependence on
time of a memory trace retrieval, gives a possibility of metamemory simulation,
generalized knowledge representation, and distinct description of conscious and
unconscious mental processes. A model of smallest inseparable part or an "atom"
of consciousness is also defined. The NNAMM's neurobiological backgrounds and
its applications to solving some interdisciplinary problems are shortly
discussed. BSDT could implement the "best neural code" used in nervous tissues
of animals and humans. | [
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] |
Many problems that arise in machine learning domain deal with nonlinearity
and quite often demand users to obtain global optimal solutions rather than
local optimal ones. Optimization problems are inherent in machine learning
algorithms and hence many methods in machine learning were inherited from the
optimization literature. Popularly known as the initialization problem, the
ideal set of parameters required will significantly depend on the given
initialization values. The recently developed TRUST-TECH (TRansformation Under
STability-reTaining Equilibria CHaracterization) methodology systematically
explores the subspace of the parameters to obtain a complete set of local
optimal solutions. In this thesis work, we propose TRUST-TECH based methods for
solving several optimization and machine learning problems. Two stages namely,
the local stage and the neighborhood-search stage, are repeated alternatively
in the solution space to achieve improvements in the quality of the solutions.
Our methods were tested on both synthetic and real datasets and the advantages
of using this novel framework are clearly manifested. This framework not only
reduces the sensitivity to initialization, but also allows the flexibility for
the practitioners to use various global and local methods that work well for a
particular problem of interest. Other hierarchical stochastic algorithms like
evolutionary algorithms and smoothing algorithms are also studied and
frameworks for combining these methods with TRUST-TECH have been proposed and
evaluated on several test systems. | [
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] |
We present a method to eliminate redundancy in the transition tables of
Boolean automata: schema redescription with two symbols. One symbol is used to
capture redundancy of individual input variables, and another to capture
permutability in sets of input variables: fully characterizing the canalization
present in Boolean functions. Two-symbol schemata explain aspects of the
behaviour of automata networks that the characterization of their emergent
patterns does not capture. We use our method to compare two well-known cellular
automata for the density classification task: the human engineered CA GKL, and
another obtained via genetic programming (GP). We show that despite having very
different collective behaviour, these rules are very similar. Indeed, GKL is a
special case of GP. Therefore, we demonstrate that it is more feasible to
compare cellular automata via schema redescriptions of their rules, than by
looking at their emergent behaviour, leading us to question the tendency in
complexity research to pay much more attention to emergent patterns than to
local interactions. | [
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