id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.01311 | TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional
Networks to Predict Transcription Factor Binding Sites | cs.LG cs.AI q-bio.GN | Transcription factors are proteins that regulate the expression of genes by
binding to specific genomic regions known as Transcription Factor Binding Sites
(TFBSs), typically located in the promoter regions of those genes. Accurate
prediction of these binding sites is essential for understanding the complex
gene regu... |
2502.01312 | CleanPose: Category-Level Object Pose Estimation via Causal Learning and
Knowledge Distillation | cs.CV | Category-level object pose estimation aims to recover the rotation,
translation and size of unseen instances within predefined categories. In this
task, deep neural network-based methods have demonstrated remarkable
performance. However, previous studies show they suffer from spurious
correlations raised by "unclean"... |
2502.01313 | Strategic Classification with Randomised Classifiers | cs.LG stat.ML | We consider the problem of strategic classification, where a learner must
build a model to classify agents based on features that have been strategically
modified. Previous work in this area has concentrated on the case when the
learner is restricted to deterministic classifiers. In contrast, we perform a
theoretical... |
2502.01315 | Optimization-based Coordination of Traffic Lights and Automated Vehicles
at Intersections | math.OC cs.SY eess.SY | This paper tackles the challenge of coordinating traffic lights and automated
vehicles at signalized intersections, formulated as a constrained
finite-horizon optimal control problem. The problem falls into the category of
mixed-integer nonlinear programming, posing challenges for solving large
instances. To address ... |
2502.01316 | Learning Fused State Representations for Control from Multi-View
Observations | cs.LG cs.AI | Multi-View Reinforcement Learning (MVRL) seeks to provide agents with
multi-view observations, enabling them to perceive environment with greater
effectiveness and precision. Recent advancements in MVRL focus on extracting
latent representations from multiview observations and leveraging them in
control tasks. Howeve... |
2502.01329 | Benchmarking Different QP Formulations and Solvers for Dynamic
Quadrupedal Walking | cs.RO | Quadratic Programs (QPs) are widely used in the control of walking robots,
especially in Model Predictive Control (MPC) and Whole-Body Control (WBC). In
both cases, the controller design requires the formulation of a QP and the
selection of a suitable QP solver, both requiring considerable time and
expertise. While c... |
2502.01330 | Accelerating Linear Recurrent Neural Networks for the Edge with
Unstructured Sparsity | cs.LG cs.NE | Linear recurrent neural networks enable powerful long-range sequence modeling
with constant memory usage and time-per-token during inference. These
architectures hold promise for streaming applications at the edge, but
deployment in resource-constrained environments requires hardware-aware
optimizations to minimize l... |
2502.01332 | A two-disk approach to the synthesis of coherent passive equalizers for
linear quantum systems | quant-ph cs.SY eess.SY math.OC | The coherent equalization problem consists in designing a quantum system
acting as a mean-square near optimal filter for a given quantum communication
channel. The paper develops an improved method for the synthesis of transfer
functions for such equalizing filters, based on a linear quantum system model
of the chann... |
2502.01334 | Deep generative computed perfusion-deficit mapping of ischaemic stroke | q-bio.QM cs.CV q-bio.NC | Focal deficits in ischaemic stroke result from impaired perfusion downstream
of a critical vascular occlusion. While parenchymal lesions are traditionally
used to predict clinical deficits, the underlying pattern of disrupted
perfusion provides information upstream of the lesion, potentially yielding
earlier predicti... |
2502.01335 | ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D
Echocardiographies | cs.CV | While traditional self-supervised learning methods improve performance and
robustness across various medical tasks, they rely on single-vector embeddings
that may not capture fine-grained concepts such as anatomical structures or
organs. The ability to identify such concepts and their characteristics without
supervis... |
2502.01337 | Neural Preconditioning Operator for Efficient PDE Solves | cs.CE | We introduce the Neural Preconditioning Operator (NPO), a novel approach
designed to accelerate Krylov solvers in solving large, sparse linear systems
derived from partial differential equations (PDEs). Unlike classical
preconditioners that often require extensive tuning and struggle to generalize
across different me... |
2502.01338 | PtyGenography: using generative models for regularization of the phase
retrieval problem | stat.ML cs.IT cs.LG math.FA math.IT math.OC | In phase retrieval and similar inverse problems, the stability of solutions
across different noise levels is crucial for applications. One approach to
promote it is using signal priors in a form of a generative model as a
regularization, at the expense of introducing a bias in the reconstruction. In
this paper, we ex... |
2502.01339 | Reducing Ciphertext and Key Sizes for MLWE-Based Cryptosystems | cs.CR cs.IT math.IT | The concatenation of encryption and decryption can be interpreted as data
transmission over a noisy communication channel. In this work, we use finite
blocklength methods (normal approximation and random coding union bound) as
well as asymptotics to show that ciphertext and key sizes of the
state-of-the-art post-quan... |
2502.01340 | Human-Agent Interaction in Synthetic Social Networks: A Framework for
Studying Online Polarization | physics.soc-ph cs.SI | Online social networks have dramatically altered the landscape of public
discourse, creating both opportunities for enhanced civic participation and
risks of deepening social divisions. Prevalent approaches to studying online
polarization have been limited by a methodological disconnect: mathematical
models excel at ... |
2502.01341 | AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal
Understanding | cs.CL | Aligning visual features with language embeddings is a key challenge in
vision-language models (VLMs). The performance of such models hinges on having
a good connector that maps visual features generated by a vision encoder to a
shared embedding space with the LLM while preserving semantic similarity.
Existing connec... |
2502.01342 | Activation by Interval-wise Dropout: A Simple Way to Prevent Neural
Networks from Plasticity Loss | cs.LG cs.AI | Plasticity loss, a critical challenge in neural network training, limits a
model's ability to adapt to new tasks or shifts in data distribution. This
paper introduces AID (Activation by Interval-wise Dropout), a novel method
inspired by Dropout, designed to address plasticity loss. Unlike Dropout, AID
generates subne... |
2502.01344 | PSSD: Making Large Language Models Self-denial via Human Psyche
Structure | cs.AI cs.CL cs.IR | The enhance of accuracy in reasoning results of LLMs arouses the community's
interests, wherein pioneering studies investigate post-hoc strategies to
rectify potential mistakes. Despite extensive efforts, they are all stuck in a
state of resource competition demanding significant time and computing
expenses. The caus... |
2502.01347 | Spurious Correlations in High Dimensional Regression: The Roles of
Regularization, Simplicity Bias and Over-Parameterization | stat.ML cs.LG | Learning models have been shown to rely on spurious correlations between
non-predictive features and the associated labels in the training data, with
negative implications on robustness, bias and fairness. In this work, we
provide a statistical characterization of this phenomenon for high-dimensional
regression, when... |
2502.01349 | Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product
Recommendations | cs.CL | The advent of Large Language Models (LLMs) has revolutionized product
recommendation systems, yet their susceptibility to adversarial manipulation
poses critical challenges, particularly in real-world commercial applications.
Our approach is the first one to tap into human psychological principles,
seamlessly modifyi... |
2502.01352 | Metric Privacy in Federated Learning for Medical Imaging: Improving
Convergence and Preventing Client Inference Attacks | cs.LG cs.CR | Federated learning is a distributed learning technique that allows training a
global model with the participation of different data owners without the need
to share raw data. This architecture is orchestrated by a central server that
aggregates the local models from the clients. This server may be trusted, but
not al... |
2502.01356 | Quasi-Conformal Convolution : A Learnable Convolution for Deep Learning
on Riemann Surfaces | cs.CV | Deep learning on non-Euclidean domains is important for analyzing complex
geometric data that lacks common coordinate systems and familiar Euclidean
properties. A central challenge in this field is to define convolution on
domains, which inherently possess irregular and non-Euclidean structures. In
this work, we intr... |
2502.01357 | Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D
Radar | cs.CV | Accurate 3D multi-object tracking (MOT) is vital for autonomous vehicles, yet
LiDAR and camera-based methods degrade in adverse weather. Meanwhile,
Radar-based solutions remain robust but often suffer from limited vertical
resolution and simplistic motion models. Existing Kalman filter-based
approaches also rely on f... |
2502.01358 | Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau
Envelopes | math.OC cs.CV cs.NA math.NA | In this article we propose a novel method for sampling from Gibbs
distributions of the form $\pi(x)\propto\exp(-U(x))$ with a potential $U(x)$.
In particular, inspired by diffusion models we propose to consider a sequence
$(\pi^{t_k})_k$ of approximations of the target density, for which
$\pi^{t_k}\approx \pi$ for $k... |
2502.01360 | A Relative Homology Theory of Representation in Neural Networks | cs.LG math.AT q-bio.NC | Previous research has proven that the set of maps implemented by neural
networks with a ReLU activation function is identical to the set of piecewise
linear continuous maps. Furthermore, such networks induce a hyperplane
arrangement splitting the input domain into convex polyhedra $G_J$ over which
the network $\Phi$ ... |
2502.01362 | Inverse Bridge Matching Distillation | cs.LG cs.CV | Learning diffusion bridge models is easy; making them fast and practical is
an art. Diffusion bridge models (DBMs) are a promising extension of diffusion
models for applications in image-to-image translation. However, like many
modern diffusion and flow models, DBMs suffer from the problem of slow
inference. To addre... |
2502.01364 | Meursault as a Data Point | cs.CY cs.AI cs.CL cs.DL cs.LG | In an era dominated by datafication, the reduction of human experiences to
quantifiable metrics raises profound philosophical and ethical questions. This
paper explores these issues through the lens of Meursault, the protagonist of
Albert Camus' The Stranger, whose emotionally detached existence epitomizes the
existe... |
2502.01366 | Trajectory World Models for Heterogeneous Environments | cs.LG | Heterogeneity in sensors and actuators across environments poses a
significant challenge to building large-scale pre-trained world models on top
of this low-dimensional sensor information. In this work, we explore
pre-training world models for heterogeneous environments by addressing key
transfer barriers in both dat... |
2502.01375 | Compact Rule-Based Classifier Learning via Gradient Descent | cs.LG cs.AI cs.LO | Rule-based models play a crucial role in scenarios that require transparency
and accountable decision-making. However, they primarily consist of discrete
parameters and structures, which presents challenges for scalability and
optimization. In this work, we introduce a new rule-based classifier trained
using gradient... |
2502.01376 | Compliance while resisting: a shear-thickening fluid controller for
physical human-robot interaction | cs.RO | Physical human-robot interaction (pHRI) is widely needed in many fields, such
as industrial manipulation, home services, and medical rehabilitation, and puts
higher demands on the safety of robots. Due to the uncertainty of the working
environment, the pHRI may receive unexpected impact interference, which affects
th... |
2502.01377 | Data-Efficient Model for Psychological Resilience Prediction based on
Neurological Data | cs.CE cs.AI | Psychological resilience, defined as the ability to rebound from adversity,
is crucial for mental health. Compared with traditional resilience assessments
through self-reported questionnaires, resilience assessments based on
neurological data offer more objective results with biological markers, hence
significantly e... |
2502.01378 | CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models | cs.LG | Large Language Models (LLMs) demonstrate exceptional performance across
various tasks but demand substantial computational resources even for
fine-tuning computation. Although Low-Rank Adaptation (LoRA) significantly
alleviates memory consumption during fine-tuning, its impact on computational
cost reduction is limit... |
2502.01382 | HingePlace: Harnessing the neural thresholding behavior to optimize
Transcranial Electrical Stimulation | eess.SY cs.SY | Transcranial Electrical Stimulation (tES) is a neuromodulation technique that
utilizes electrodes on the scalp to stimulate target brain regions. tES has
shown promise in treating many neurological conditions, such as stroke
rehabilitation and chronic pain. Several electrode placement algorithms have
been proposed to... |
2502.01383 | InfoBridge: Mutual Information estimation via Bridge Matching | cs.LG stat.ML | Diffusion bridge models have recently become a powerful tool in the field of
generative modeling. In this work, we leverage their power to address another
important problem in machine learning and information theory - the estimation
of the mutual information (MI) between two random variables. We show that by
using th... |
2502.01384 | Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods | stat.ML cs.AI cs.CL cs.LG | Discrete diffusion models have recently gained significant attention due to
their ability to process complex discrete structures for language modeling.
However, fine-tuning these models with policy gradient methods, as is commonly
done in Reinforcement Learning from Human Feedback (RLHF), remains a
challenging task. ... |
2502.01385 | Detecting Backdoor Samples in Contrastive Language Image Pretraining | cs.LG cs.CV | Contrastive language-image pretraining (CLIP) has been found to be vulnerable
to poisoning backdoor attacks where the adversary can achieve an almost perfect
attack success rate on CLIP models by poisoning only 0.01\% of the training
dataset. This raises security concerns on the current practice of pretraining
large-... |
2502.01386 | Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks
to Retrieval-Augmented Generation Models | cs.CL cs.CR cs.IR | Retrieval-Augmented Generation (RAG) systems based on Large Language Models
(LLMs) have become essential for tasks such as question answering and content
generation. However, their increasing impact on public opinion and information
dissemination has made them a critical focus for security research due to
inherent vu... |
2502.01387 | TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep
Reinforcement Learning | cs.AI cs.RO | Although Deep Reinforcement Learning (DRL) and Large Language Models (LLMs)
each show promise in addressing decision-making challenges in autonomous
driving, DRL often suffers from high sample complexity, while LLMs have
difficulty ensuring real-time decision making. To address these limitations, we
propose TeLL-Driv... |
2502.01390 | Plan-Then-Execute: An Empirical Study of User Trust and Team Performance
When Using LLM Agents As A Daily Assistant | cs.HC cs.CL | Since the explosion in popularity of ChatGPT, large language models (LLMs)
have continued to impact our everyday lives. Equipped with external tools that
are designed for a specific purpose (e.g., for flight booking or an alarm
clock), LLM agents exercise an increasing capability to assist humans in their
daily work.... |
2502.01391 | Learning Traffic Anomalies from Generative Models on Real-Time
Observations | cs.LG cs.AI cs.CV | Accurate detection of traffic anomalies is crucial for effective urban
traffic management and congestion mitigation. We use the Spatiotemporal
Generative Adversarial Network (STGAN) framework combining Graph Neural
Networks and Long Short-Term Memory networks to capture complex spatial and
temporal dependencies in tr... |
2502.01397 | Can message-passing GNN approximate triangular factorizations of sparse
matrices? | cs.LG cs.AI cs.NA math.NA | We study fundamental limitations of Graph Neural Networks (GNNs) for learning
sparse matrix preconditioners. While recent works have shown promising results
using GNNs to predict incomplete factorizations, we demonstrate that the local
nature of message passing creates inherent barriers for capturing non-local
depend... |
2502.01401 | Evolving Symbolic 3D Visual Grounder with Weakly Supervised Reflection | cs.CV | 3D visual grounding (3DVG) is challenging because of the requirement of
understanding on visual information, language and spatial relationships. While
supervised approaches have achieved superior performance, they are constrained
by the scarcity and high cost of 3D vision-language datasets. On the other
hand, LLM/VLM... |
2502.01402 | Annotation Tool and Dataset for Fact-Checking Podcasts | cs.CL | Podcasts are a popular medium on the web, featuring diverse and multilingual
content that often includes unverified claims. Fact-checking podcasts is a
challenging task, requiring transcription, annotation, and claim verification,
all while preserving the contextual details of spoken content. Our tool offers
a novel ... |
2502.01403 | AdaSVD: Adaptive Singular Value Decomposition for Large Language Models | cs.CV cs.AI cs.CL | Large language models (LLMs) have achieved remarkable success in natural
language processing (NLP) tasks, yet their substantial memory requirements
present significant challenges for deployment on resource-constrained devices.
Singular Value Decomposition (SVD) has emerged as a promising compression
technique for LLM... |
2502.01405 | FourieRF: Few-Shot NeRFs via Progressive Fourier Frequency Control | cs.CV | In this work, we introduce FourieRF, a novel approach for achieving fast and
high-quality reconstruction in the few-shot setting. Our method effectively
parameterizes features through an explicit curriculum training procedure,
incrementally increasing scene complexity during optimization. Experimental
results show th... |
2502.01406 | GRADIEND: Monosemantic Feature Learning within Neural Networks Applied
to Gender Debiasing of Transformer Models | cs.LG cs.AI cs.CL | AI systems frequently exhibit and amplify social biases, including gender
bias, leading to harmful consequences in critical areas. This study introduces
a novel encoder-decoder approach that leverages model gradients to learn a
single monosemantic feature neuron encoding gender information. We show that
our method ca... |
2502.01411 | Human Body Restoration with One-Step Diffusion Model and A New Benchmark | cs.CV | Human body restoration, as a specific application of image restoration, is
widely applied in practice and plays a vital role across diverse fields.
However, thorough research remains difficult, particularly due to the lack of
benchmark datasets. In this study, we propose a high-quality dataset automated
cropping and ... |
2502.01416 | Categorical Schr\"odinger Bridge Matching | cs.LG | The Schr\"odinger Bridge (SB) is a powerful framework for solving generative
modeling tasks such as unpaired domain translation. Most SB-related research
focuses on continuous data space $\mathbb{R}^{D}$ and leaves open theoretical
and algorithmic questions about applying SB methods to discrete data, e.g, on
finite s... |
2502.01417 | Originality in scientific titles and abstracts can predict citation
count | cs.DL cs.CL | In this research-in-progress paper, we apply a computational measure
correlating with originality from creativity science: Divergent Semantic
Integration (DSI), to a selection of 99,557 scientific abstracts and titles
selected from the Web of Science. We observe statistically significant
differences in DSI between su... |
2502.01418 | Assessing the use of Diffusion models for motion artifact correction in
brain MRI | eess.IV cs.CV cs.LG cs.NA math.NA | Magnetic Resonance Imaging generally requires long exposure times, while
being sensitive to patient motion, resulting in artifacts in the acquired
images, which may hinder their diagnostic relevance. Despite research efforts
to decrease the acquisition time, and designing efficient acquisition
sequences, motion artif... |
2502.01419 | Visual Attention Never Fades: Selective Progressive Attention
ReCalibration for Detailed Image Captioning in Multimodal Large Language
Models | cs.CV cs.AI | Detailed image captioning is essential for tasks like data generation and
aiding visually impaired individuals. High-quality captions require a balance
between precision and recall, which remains challenging for current multimodal
large language models (MLLMs). In this work, we hypothesize that this
limitation stems ... |
2502.01425 | The Batch Complexity of Bandit Pure Exploration | cs.LG stat.ML | In a fixed-confidence pure exploration problem in stochastic multi-armed
bandits, an algorithm iteratively samples arms and should stop as early as
possible and return the correct answer to a query about the arms distributions.
We are interested in batched methods, which change their sampling behaviour
only a few tim... |
2502.01427 | Structural features of the fly olfactory circuit mitigate the
stability-plasticity dilemma in continual learning | cs.LG cs.AI cs.CV q-bio.NC | Artificial neural networks face the stability-plasticity dilemma in continual
learning, while the brain can maintain memories and remain adaptable. However,
the biological strategies for continual learning and their potential to inspire
learning algorithms in neural networks are poorly understood. This study
presents... |
2502.01429 | An Algorithm for Fixed Budget Best Arm Identification with Combinatorial
Exploration | cs.LG | We consider the best arm identification (BAI) problem in the $K-$armed bandit
framework with a modification - the agent is allowed to play a subset of arms
at each time slot instead of one arm. Consequently, the agent observes the
sample average of the rewards of the arms that constitute the probed subset.
Several tr... |
2502.01430 | Molecular Odor Prediction Based on Multi-Feature Graph Attention
Networks | cs.LG q-bio.QM | Olfactory perception plays a critical role in both human and organismal
interactions, yet understanding of its underlying mechanisms and influencing
factors remain insufficient. Molecular structures influence odor perception
through intricate biochemical interactions, and accurately quantifying
structure-odor relatio... |
2502.01432 | Emergent Stack Representations in Modeling Counter Languages Using
Transformers | cs.CL cs.LG | Transformer architectures are the backbone of most modern language models,
but understanding the inner workings of these models still largely remains an
open problem. One way that research in the past has tackled this problem is by
isolating the learning capabilities of these architectures by training them
over well-... |
2502.01436 | Towards Safer Chatbots: A Framework for Policy Compliance Evaluation of
Custom GPTs | cs.CL cs.AI | Large Language Models (LLMs) have gained unprecedented prominence, achieving
widespread adoption across diverse domains and integrating deeply into society.
The capability to fine-tune general-purpose LLMs, such as Generative
Pre-trained Transformers (GPT), for specific tasks has facilitated the
emergence of numerous... |
2502.01439 | Alternating direction method of multipliers for polynomial optimization | math.OC cs.SY eess.SY | Multivariate polynomial optimization is a prevalent model for a number of
engineering problems. From a mathematical viewpoint, polynomial optimization is
challenging because it is non-convex. The Lasserre's theory, based on
semidefinite relaxations, provides an effective tool to overcome this issue and
to achieve the... |
2502.01441 | Improved Training Technique for Latent Consistency Models | cs.CV cs.LG | Consistency models are a new family of generative models capable of producing
high-quality samples in either a single step or multiple steps. Recently,
consistency models have demonstrated impressive performance, achieving results
on par with diffusion models in the pixel space. However, the success of
scaling consis... |
2502.01445 | SPFFNet: Strip Perception and Feature Fusion Spatial Pyramid Pooling for
Fabric Defect Detection | cs.CV cs.AI | Defect detection in fabrics is critical for quality control, yet existing
methods often struggle with complex backgrounds and shape-specific defects. In
this paper, we propose an improved fabric defect detection model based on
YOLOv11. To enhance the detection of strip defects, we introduce a Strip
Perception Module ... |
2502.01448 | What Can You Say to a Robot? Capability Communication Leads to More
Natural Conversations | cs.RO cs.HC | When encountering a robot in the wild, it is not inherently clear to human
users what the robot's capabilities are. When encountering misunderstandings or
problems in spoken interaction, robots often just apologize and move on,
without additional effort to make sure the user understands what happened. We
set out to c... |
2502.01450 | Simulating Rumor Spreading in Social Networks using LLM Agents | cs.SI cs.AI | With the rise of social media, misinformation has become increasingly
prevalent, fueled largely by the spread of rumors. This study explores the use
of Large Language Model (LLM) agents within a novel framework to simulate and
analyze the dynamics of rumor propagation across social networks. To this end,
we design a ... |
2502.01455 | Temporal-consistent CAMs for Weakly Supervised Video Segmentation in
Waste Sorting | cs.CV cs.AI cs.LG | In industrial settings, weakly supervised (WS) methods are usually preferred
over their fully supervised (FS) counterparts as they do not require costly
manual annotations. Unfortunately, the segmentation masks obtained in the WS
regime are typically poor in terms of accuracy. In this work, we present a WS
method cap... |
2502.01456 | Process Reinforcement through Implicit Rewards | cs.LG cs.AI cs.CL | Dense process rewards have proven a more effective alternative to the sparse
outcome-level rewards in the inference-time scaling of large language models
(LLMs), particularly in tasks requiring complex multi-step reasoning. While
dense rewards also offer an appealing choice for the reinforcement learning
(RL) of LLMs... |
2502.01458 | Understanding the Capabilities and Limitations of Weak-to-Strong
Generalization | cs.LG stat.ML | Weak-to-strong generalization, where weakly supervised strong models
outperform their weaker teachers, offers a promising approach to aligning
superhuman models with human values. To deepen the understanding of this
approach, we provide theoretical insights into its capabilities and
limitations. First, in the classif... |
2502.01459 | Learning to Partially Defer for Sequences | stat.ME cs.LG stat.ML | In the Learning to Defer (L2D) framework, a prediction model can either make
a prediction or defer it to an expert, as determined by a rejector. Current L2D
methods train the rejector to decide whether to reject the entire prediction,
which is not desirable when the model predicts long sequences. We present an
L2D se... |
2502.01461 | Docking-Aware Attention: Dynamic Protein Representations through
Molecular Context Integration | cs.LG q-bio.BM | Computational prediction of enzymatic reactions represents a crucial
challenge in sustainable chemical synthesis across various scientific domains,
ranging from drug discovery to materials science and green chemistry. These
syntheses rely on proteins that selectively catalyze complex molecular
transformations. These ... |
2502.01464 | Predicting symmetries of quantum dynamics with optimal samples | quant-ph cs.IT math.IT | Identifying symmetries in quantum dynamics, such as identity or time-reversal
invariance, is a crucial challenge with profound implications for quantum
technologies. We introduce a unified framework combining group representation
theory and subgroup hypothesis testing to predict these symmetries with optimal
efficien... |
2502.01465 | Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics
Motions | cs.RO cs.SY eess.SY | Previous humanoid robot research works treat the robot as a bipedal mobile
manipulation platform, where only the feet and hands contact the environment.
However, we humans use all body parts to interact with the world, e.g., we sit
in chairs, get up from the ground, or roll on the floor. Contacting the
environment us... |
2502.01467 | Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis | cs.CV | Multi-modal image fusion synthesizes information from multiple sources into a
single image, facilitating downstream tasks such as semantic segmentation.
Current approaches primarily focus on acquiring informative fusion images at
the visual display stratum through intricate mappings. Although some approaches
attempt ... |
2502.01472 | FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal
Unalignment for Large Language Model | cs.CL cs.AI | Large language models have been widely applied, but can inadvertently encode
sensitive or harmful information, raising significant safety concerns. Machine
unlearning has emerged to alleviate this concern; however, existing
training-time unlearning approaches, relying on coarse-grained loss
combinations, have limitat... |
2502.01473 | Generalization Error Analysis for Selective State-Space Models Through
the Lens of Attention | cs.LG | State-space models (SSMs) are a new class of foundation models that have
emerged as a compelling alternative to Transformers and their attention
mechanisms for sequence processing tasks. This paper provides a detailed
theoretical analysis of selective SSMs, the core components of the Mamba and
Mamba-2 architectures. ... |
2502.01474 | Simultaneous Automatic Picking and Manual Picking Refinement for
First-Break | cs.CV eess.IV | First-break picking is a pivotal procedure in processing microseismic data
for geophysics and resource exploration. Recent advancements in deep learning
have catalyzed the evolution of automated methods for identifying first-break.
Nevertheless, the complexity of seismic data acquisition and the requirement
for detai... |
2502.01476 | Neuro-Symbolic AI for Analytical Solutions of Differential Equations | cs.LG | Analytical solutions of differential equations offer exact insights into
fundamental behaviors of physical processes. Their application, however, is
limited as finding these solutions is difficult. To overcome this limitation,
we combine two key insights. First, constructing an analytical solution
requires a composit... |
2502.01477 | Position: Empowering Time Series Reasoning with Multimodal LLMs | cs.LG cs.AI | Understanding time series data is crucial for multiple real-world
applications. While large language models (LLMs) show promise in time series
tasks, current approaches often rely on numerical data alone, overlooking the
multimodal nature of time-dependent information, such as textual descriptions,
visual data, and a... |
2502.01478 | BYON: Bring Your Own Networks for Digital Agriculture Applications | cs.NI cs.RO | Digital agriculture technologies rely on sensors, drones, robots, and
autonomous farm equipment to improve farm yields and incorporate sustainability
practices. However, the adoption of such technologies is severely limited by
the lack of broadband connectivity in rural areas. We argue that farming
applications do no... |
2502.01481 | Explaining Context Length Scaling and Bounds for Language Models | cs.LG cs.CL | Long Context Language Models have drawn great attention in the past few
years. There has been work discussing the impact of long context on Language
Model performance: some find that long irrelevant context could harm
performance, while some experimentally summarize loss reduction by relevant
long context as Scaling ... |
2502.01482 | On the Uncertainty of a Simple Estimator for Remote Source Monitoring
over ALOHA Channels | cs.IT math.IT | Efficient remote monitoring of distributed sources is essential for many
Internet of Things (IoT) applications. This work studies the uncertainty at the
receiver when tracking two-state Markov sources over a slotted random access
channel without feedback, using the conditional entropy as a performance
indicator, and ... |
2502.01484 | Robot Cell Modeling via Exploratory Robot Motions | cs.RO | Generating a collision-free robot motion is crucial for safe applications in
real-world settings. This requires an accurate model of all obstacle shapes
within the constrained robot cell, which is particularly challenging and
time-consuming. The difficulty is heightened in flexible production lines,
where the environ... |
2502.01490 | MoireDB: Formula-generated Interference-fringe Image Dataset | cs.CV cs.AI cs.LG | Image recognition models have struggled to treat recognition robustness to
real-world degradations. In this context, data augmentation methods like PixMix
improve robustness but rely on generative arts and feature visualizations
(FVis), which have copyright, drawing cost, and scalability issues. We propose
MoireDB, a... |
2502.01491 | Memorization Inheritance in Sequence-Level Knowledge Distillation for
Neural Machine Translation | cs.CL | In this work, we explore how instance-level memorization in the teacher
Neural Machine Translation (NMT) model gets inherited by the student model in
sequence-level knowledge distillation (SeqKD). We find that despite not
directly seeing the original training data, students memorize more than
baseline models (models ... |
2502.01492 | Develop AI Agents for System Engineering in Factorio | cs.AI | Continuing advances in frontier model research are paving the way for
widespread deployment of AI agents. Meanwhile, global interest in building
large, complex systems in software, manufacturing, energy and logistics has
never been greater. Although AI driven system engineering holds tremendous
promise, the static be... |
2502.01498 | Compact Yet Highly Accurate Printed Classifiers Using Sequential Support
Vector Machine Circuits | cs.LG cs.AR | Printed Electronics (PE) technology has emerged as a promising alternative to
silicon-based computing. It offers attractive properties such as on-demand
ultra-low-cost fabrication, mechanical flexibility, and conformality. However,
PE are governed by large feature sizes, prohibiting the realization of complex
printed... |
2502.01500 | Gamma/hadron separation in the TAIGA experiment with neural network
methods | astro-ph.IM astro-ph.HE cs.LG | In this work, the ability of rare VHE gamma ray selection with neural network
methods is investigated in the case when cosmic radiation flux strongly
prevails (ratio up to {10^4} over the gamma radiation flux from a point
source). This ratio is valid for the Crab Nebula in the TeV energy range, since
the Crab is a we... |
2502.01503 | Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark
Vessel Trajectories | cs.AI cs.LG cs.LO cs.SC | Bad actors in the maritime industry engage in illegal behaviors after
disabling their vessel's automatic identification system (AIS) - which makes
finding such vessels difficult for analysts. Machine learning approaches only
succeed in identifying the locations of these ``dark vessels'' in the immediate
future. This ... |
2502.01506 | TwinMarket: A Scalable Behavioral and Social Simulation for Financial
Markets | cs.CE cs.CY | The study of social emergence has long been a central focus in social
science. Traditional modeling approaches, such as rule-based Agent-Based Models
(ABMs), struggle to capture the diversity and complexity of human behavior,
particularly the irrational factors emphasized in behavioral economics.
Recently, large lang... |
2502.01507 | End-to-end Training for Text-to-Image Synthesis using Dual-Text
Embeddings | cs.CV | Text-to-Image (T2I) synthesis is a challenging task that requires modeling
complex interactions between two modalities ( i.e., text and image). A common
framework adopted in recent state-of-the-art approaches to achieving such
multimodal interactions is to bootstrap the learning process with pre-trained
image-aligned... |
2502.01510 | Grid-based exoplanet atmospheric mass loss predictions through neural
network | astro-ph.EP cs.LG | The fast and accurate estimation of planetary mass-loss rates is critical for
planet population and evolution modelling. We use machine learning (ML) for
fast interpolation across an existing large grid of hydrodynamic upper
atmosphere models, providing mass-loss rates for any planet inside the grid
boundaries with s... |
2502.01512 | Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices | stat.ME cs.LG math.ST stat.ML stat.TH | Circular and non-flat data distributions are prevalent across diverse domains
of data science, yet their specific geometric structures often remain
underutilized in machine learning frameworks. A principled approach to
accounting for the underlying geometry of such data is pivotal, particularly
when extending statist... |
2502.01517 | Regularized interpolation in 4D neural fields enables optimization of 3D
printed geometries | cs.GR cs.AI | The ability to accurately produce geometries with specified properties is
perhaps the most important characteristic of a manufacturing process. 3D
printing is marked by exceptional design freedom and complexity but is also
prone to geometric and other defects that must be resolved for it to reach its
full potential. ... |
2502.01518 | Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using
BERT and Character-Level CNN | cs.CL cs.LG cs.SI | Smishing is a social engineering attack using SMS containing malicious
content to deceive individuals into disclosing sensitive information or
transferring money to cybercriminals. Smishing attacks have surged by 328%,
posing a major threat to mobile users, with losses exceeding \$54.2 million in
2019. Despite its gr... |
2502.01520 | Prioritizing App Reviews for Developer Responses on Google Play | cs.SE cs.LG | The number of applications in Google Play has increased dramatically in
recent years. On Google Play, users can write detailed reviews and rate apps,
with these ratings significantly influencing app success and download numbers.
Reviews often include notable information like feature requests, which are
valuable for s... |
2502.01521 | Toward Task Generalization via Memory Augmentation in Meta-Reinforcement
Learning | cs.LG cs.AI cs.RO | In reinforcement learning (RL), agents often struggle to perform well on
tasks that differ from those encountered during training. This limitation
presents a challenge to the broader deployment of RL in diverse and dynamic
task settings. In this work, we introduce memory augmentation, a memory-based
RL approach to im... |
2502.01522 | BD-Diff: Generative Diffusion Model for Image Deblurring on Unknown
Domains with Blur-Decoupled Learning | cs.CV | Generative diffusion models trained on large-scale datasets have achieved
remarkable progress in image synthesis. In favor of their ability to supplement
missing details and generate aesthetically pleasing contents, recent works have
applied them to image deblurring tasks via training an adapter on blurry-sharp
image... |
2502.01523 | CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question
Answering | cs.CL | Large language models (LLMs) are prone to hallucinations in
question-answering (QA) tasks when faced with ambiguous questions. Users often
assume that LLMs share their cognitive alignment, a mutual understanding of
context, intent, and implicit details, leading them to omit critical
information in the queries. Howeve... |
2502.01524 | Efficiently Integrate Large Language Models with Visual Perception: A
Survey from the Training Paradigm Perspective | cs.CV cs.AI cs.CL cs.LG | The integration of vision-language modalities has been a significant focus in
multimodal learning, traditionally relying on Vision-Language Pretrained
Models. However, with the advent of Large Language Models (LLMs), there has
been a notable shift towards incorporating LLMs with vision modalities.
Following this, the... |
2502.01527 | Enhancing Bayesian Network Structural Learning with Monte Carlo Tree
Search | cs.LG | This article presents MCTS-BN, an adaptation of the Monte Carlo Tree Search
(MCTS) algorithm for the structural learning of Bayesian Networks (BNs).
Initially designed for game tree exploration, MCTS has been repurposed to
address the challenge of learning BN structures by exploring the search space
of potential ance... |
2502.01528 | SQUASH: Serverless and Distributed Quantization-based Attributed Vector
Similarity Search | cs.DC cs.DB | Vector similarity search presents significant challenges in terms of
scalability for large and high-dimensional datasets, as well as in providing
native support for hybrid queries. Serverless computing and cloud functions
offer attractive benefits such as elasticity and cost-effectiveness, but are
difficult to apply ... |
2502.01530 | The in-context inductive biases of vision-language models differ across
modalities | cs.CV cs.CL cs.LG | Inductive biases are what allow learners to make guesses in the absence of
conclusive evidence. These biases have often been studied in cognitive science
using concepts or categories -- e.g. by testing how humans generalize a new
category from a few examples that leave the category boundary ambiguous. We use
these ap... |
2502.01532 | Federated Learning with Discriminative Naive Bayes Classifier | cs.LG | Federated Learning has emerged as a promising approach to train machine
learning models on decentralized data sources while preserving data privacy.
This paper proposes a new federated approach for Naive Bayes (NB)
classification, assuming discrete variables. Our approach federates a
discriminative variant of NB, sha... |
2502.01533 | Transformers trained on proteins can learn to attend to Euclidean
distance | cs.LG cs.AI q-bio.BM | While conventional Transformers generally operate on sequence data, they can
be used in conjunction with structure models, typically SE(3)-invariant or
equivariant graph neural networks (GNNs), for 3D applications such as protein
structure modelling. These hybrids typically involve either (1)
preprocessing/tokenizing... |
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