id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.07851 | Fast and Safe Scheduling of Robots | cs.RO | In this paper, we present an experimental analysis of a fast heuristic
algorithm that was designed to generate a fast, collision-free schedule for a
set of robots on a path graph. The experiments confirm the algorithm's
effectiveness in producing collision-free schedules as well as achieving the
optimal solution when... |
2502.07852 | Fresh2comm: Information Freshness Optimized Collaborative Perception | cs.MA | Collaborative perception is a cornerstone of intelligent connected vehicles,
enabling them to share and integrate sensory data to enhance situational
awareness. However, measuring the impact of high transmission delay and
inconsistent delay on collaborative perception in real communication scenarios,
as well as impro... |
2502.07853 | PolicySimEval: A Benchmark for Evaluating Policy Outcomes through
Agent-Based Simulation | cs.MA cs.CY | With the growing adoption of agent-based models in policy evaluation, a
pressing question arises: Can such systems effectively simulate and analyze
complex social scenarios to inform policy decisions? Addressing this challenge
could significantly enhance the policy-making process, offering researchers and
practitione... |
2502.07854 | Advancing Heat Demand Forecasting with Attention Mechanisms:
Opportunities and Challenges | cs.LG cs.CV | Global leaders and policymakers are unified in their unequivocal commitment
to decarbonization efforts in support of Net-Zero agreements. District Heating
Systems (DHS), while contributing to carbon emissions due to the continued
reliance on fossil fuels for heat production, are embracing more sustainable
practices a... |
2502.07855 | Vision-Language Models for Edge Networks: A Comprehensive Survey | cs.CV cs.AI cs.CL | Vision Large Language Models (VLMs) combine visual understanding with natural
language processing, enabling tasks like image captioning, visual question
answering, and video analysis. While VLMs show impressive capabilities across
domains such as autonomous vehicles, smart surveillance, and healthcare, their
deployme... |
2502.07856 | MRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE
Solvers | cs.CV cs.AI cs.LG | In applications of diffusion models, controllable generation is of practical
significance, but is also challenging. Current methods for controllable
generation primarily focus on modifying the score function of diffusion models,
while Mean Reverting (MR) Diffusion directly modifies the structure of the
stochastic dif... |
2502.07857 | SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect
Estimation With an Unknown Graph | stat.ML cs.AI cs.LG | Causal discovery can be computationally demanding for large numbers of
variables. If we only wish to estimate the causal effects on a small subset of
target variables, we might not need to learn the causal graph for all
variables, but only a small subgraph that includes the targets and their
adjustment sets. In this ... |
2502.07858 | MAAT: Mamba Adaptive Anomaly Transformer with association discrepancy
for time series | cs.LG | Anomaly detection in time series is essential for industrial monitoring and
environmental sensing, yet distinguishing anomalies from complex patterns
remains challenging. Existing methods like the Anomaly Transformer and
DCdetector have progressed, but they face limitations such as sensitivity to
short-term contexts ... |
2502.07859 | Automatic Prostate Volume Estimation in Transabdominal Ultrasound Images | eess.IV cs.CV | Prostate cancer is a leading health concern among men, requiring accurate and
accessible methods for early detection and risk stratification. Prostate volume
(PV) is a key parameter in multivariate risk stratification for early prostate
cancer detection, commonly estimated using transrectal ultrasound (TRUS). While
T... |
2502.07861 | BalanceKV: KV Cache Compression through Discrepancy Theory | cs.LG cs.AI cs.DS | Large language models (LLMs) have achieved impressive success, but their high
memory requirements present challenges for long-context token generation. The
memory complexity of long-context LLMs is primarily due to the need to store
Key-Value (KV) embeddings in their KV cache. We present BalanceKV, a KV cache
compres... |
2502.07862 | ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise
and Compute Resources | cs.LG cs.AI cs.CV | Multimodal deep learning systems are deployed in dynamic scenarios due to the
robustness afforded by multiple sensing modalities. Nevertheless, they struggle
with varying compute resource availability (due to multi-tenancy, device
heterogeneity, etc.) and fluctuating quality of inputs (from sensor feed
corruption, en... |
2502.07864 | TransMLA: Multi-Head Latent Attention Is All You Need | cs.LG cs.AI | Modern large language models (LLMs) often encounter communication bottlenecks
on current hardware, rather than purely computational constraints. Multi-head
Latent Attention (MLA) tackles this challenge by using low-rank matrices in the
key-value (KV) layers, thereby allowing compressed latent KV states to be
cached. ... |
2502.07866 | Design and Implementation of Scalable Communication Interfaces for
Reliable and Stable Real-time Co-Simulation of Power Systems | eess.SY cs.SY | Co-simulation offers an integrated approach for modeling the large-scale
integration of inverter-based resources (IBRs) into transmission and
distribution grids. This paper presents a scalable communication interface
design and implementation to enable reliable and stable real-time co-simulation
of power systems with... |
2502.07869 | EventEgo3D++: 3D Human Motion Capture from a Head-Mounted Event Camera | cs.CV | Monocular egocentric 3D human motion capture remains a significant challenge,
particularly under conditions of low lighting and fast movements, which are
common in head-mounted device applications. Existing methods that rely on RGB
cameras often fail under these conditions. To address these limitations, we
introduce ... |
2502.07870 | TextAtlas5M: A Large-scale Dataset for Dense Text Image Generation | cs.CV | Text-conditioned image generation has gained significant attention in recent
years and are processing increasingly longer and comprehensive text prompt. In
everyday life, dense and intricate text appears in contexts like
advertisements, infographics, and signage, where the integration of both text
and visuals is esse... |
2502.07889 | A unifying account of warm start guarantees for patches of quantum
landscapes | quant-ph cs.LG stat.ML | Barren plateaus are fundamentally a statement about quantum loss landscapes
on average but there can, and generally will, exist patches of barren plateau
landscapes with substantial gradients. Previous work has studied certain
classes of parameterized quantum circuits and found example regions where
gradients vanish ... |
2502.07891 | The Observational Partial Order of Causal Structures with Latent
Variables | stat.ML cs.LG quant-ph | For two causal structures with the same set of visible variables, one is said
to observationally dominate the other if the set of distributions over the
visible variables realizable by the first contains the set of distributions
over the visible variables realizable by the second. Knowing such dominance
relations is ... |
2502.07904 | Intelligent Legal Assistant: An Interactive Clarification System for
Legal Question Answering | cs.CL | The rise of large language models has opened new avenues for users seeking
legal advice. However, users often lack professional legal knowledge, which can
lead to questions that omit critical information. This deficiency makes it
challenging for traditional legal question-answering systems to accurately
identify user... |
2502.07905 | DeepSeek on a Trip: Inducing Targeted Visual Hallucinations via
Representation Vulnerabilities | cs.CV cs.LG | Multimodal Large Language Models (MLLMs) represent the cutting edge of AI
technology, with DeepSeek models emerging as a leading open-source alternative
offering competitive performance to closed-source systems. While these models
demonstrate remarkable capabilities, their vision-language integration
mechanisms intro... |
2502.07912 | Elevating Legal LLM Responses: Harnessing Trainable Logical Structures
and Semantic Knowledge with Legal Reasoning | cs.CL | Large Language Models (LLMs) have achieved impressive results across numerous
domains, yet they experience notable deficiencies in legal question-answering
tasks. LLMs often generate generalized responses that lack the logical
specificity required for expert legal advice and are prone to hallucination,
providing answ... |
2502.07922 | Visual-Haptic Model Mediated Teleoperation for Remote Ultrasound | cs.RO cs.HC | Tele-ultrasound has the potential greatly to improve health equity for
countless remote communities. However, practical scenarios involve potentially
large time delays which cause current implementations of telerobotic ultrasound
(US) to fail. Using a local model of the remote environment to provide haptics
to the ex... |
2502.07923 | Sign Operator for Coping with Heavy-Tailed Noise: High Probability
Convergence Bounds with Extensions to Distributed Optimization and Comparison
Oracle | math.OC cs.LG | The growing popularity of AI optimization problems involving severely
corrupted data has increased the demand for methods capable of handling
heavy-tailed noise, i.e., noise with bounded $\kappa$-th moment, $\kappa \in
(1,2]$. For the widely used clipping technique, effectiveness heavily depends
on the careful tuning... |
2502.07924 | NDAI Agreements | econ.TH cs.AI | We study a fundamental challenge in the economics of innovation: an inventor
must reveal details of a new idea to secure compensation or funding, yet such
disclosure risks expropriation. We present a model in which a seller (inventor)
and buyer (investor) bargain over an information good under the threat of
hold-up. ... |
2502.07931 | Educating a Responsible AI Workforce: Piloting a Curricular Module on AI
Policy in a Graduate Machine Learning Course | cs.CY cs.AI | As artificial intelligence (AI) technologies begin to permeate diverse
fields-from healthcare to education-consumers, researchers and policymakers are
increasingly raising concerns about whether and how AI is regulated. It is
therefore reasonable to anticipate that alignment with principles of 'ethical'
or 'responsib... |
2502.07934 | Age of Information Optimization with Preemption Strategies for
Correlated Systems | cs.IT math.IT | In this paper, we examine a multi-sensor system where each sensor monitors
multiple dynamic information processes and transmits updates over a shared
communication channel. These updates may include correlated information across
the various processes. In this type of system, we analyze the impact of
preemption, where... |
2502.07937 | Active Advantage-Aligned Online Reinforcement Learning with Offline Data | cs.LG stat.ML | Online reinforcement learning (RL) enhances policies through direct
interactions with the environment, but faces challenges related to sample
efficiency. In contrast, offline RL leverages extensive pre-collected data to
learn policies, but often produces suboptimal results due to limited data
coverage. Recent efforts... |
2502.07938 | Adapting Multilingual Embedding Models to Historical Luxembourgish | cs.CL | The growing volume of digitized historical texts requires effective semantic
search using text embeddings. However, pre-trained multilingual models,
typically evaluated on contemporary texts, face challenges with historical
digitized content due to OCR noise and outdated spellings. We explore the use
of multilingual ... |
2502.07939 | Discrete Markov Probabilistic Models | stat.ML cs.LG | This paper introduces the Discrete Markov Probabilistic Model (DMPM), a novel
algorithm for discrete data generation. The algorithm operates in the space of
bits $\{0,1\}^d$, where the noising process is a continuous-time Markov chain
that can be sampled exactly via a Poissonian clock that flips labels uniformly
at r... |
2502.07942 | Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths
of Large and Small LLMs | cs.MA cs.LG | Web browsing agents powered by large language models (LLMs) have shown
tremendous potential in automating complex web-based tasks. Existing approaches
typically rely on large LLMs (e.g., GPT-4o) to explore web environments and
generate trajectory data, which is then used either for demonstration retrieval
(for large ... |
2502.07943 | CREDAL: Close Reading of Data Models | cs.DB cs.AI cs.CY | Data models are necessary for the birth of data and of any data-driven
system. Indeed, every algorithm, every machine learning model, every
statistical model, and every database has an underlying data model without
which the system would not be usable. Hence, data models are excellent sites
for interrogating the (mat... |
2502.07944 | SHACL-SKOS Based Knowledge Representation of Material Safety Data Sheet
(SDS) for the Pharmaceutical Industry | cs.AI | We report the development of a knowledge representation and reasoning (KRR)
system built on hybrid SHACL-SKOS ontologies for globally harmonized system
(GHS) material Safety Data Sheets (SDS) to enhance chemical safety
communication and regulatory compliance. SDS are comprehensive documents
containing safety and hand... |
2502.07945 | SurGrID: Controllable Surgical Simulation via Scene Graph to Image
Diffusion | cs.CV cs.LG | Surgical simulation offers a promising addition to conventional surgical
training. However, available simulation tools lack photorealism and rely on
hardcoded behaviour. Denoising Diffusion Models are a promising alternative for
high-fidelity image synthesis, but existing state-of-the-art conditioning
methods fall sh... |
2502.07949 | VSC-RL: Advancing Autonomous Vision-Language Agents with Variational
Subgoal-Conditioned Reinforcement Learning | cs.LG cs.AI | State-of-the-art (SOTA) reinforcement learning (RL) methods enable the
vision-language agents to learn from interactions with the environment without
human supervision. However, they struggle with learning inefficiencies in
tackling real-world complex sequential decision-making tasks, especially with
sparse reward si... |
2502.07951 | Federated Self-supervised Domain Generalization for Label-efficient
Polyp Segmentation | cs.CV cs.DC cs.LG | Employing self-supervised learning (SSL) methodologies assumes par-amount
significance in handling unlabeled polyp datasets when building deep
learning-based automatic polyp segmentation models. However, the intricate
privacy dynamics surrounding medical data often preclude seamless data sharing
among disparate medic... |
2502.07957 | Intrinsic Bias is Predicted by Pretraining Data and Correlates with
Downstream Performance in Vision-Language Encoders | cs.AI | While recent work has found that vision-language models trained under the
Contrastive Language Image Pre-training (CLIP) framework contain intrinsic
social biases, the extent to which different upstream pre-training features of
the framework relate to these biases, and hence how intrinsic bias and
downstream performa... |
2502.07962 | ESPFormer: Doubly-Stochastic Attention with Expected Sliced Transport
Plans | cs.LG | While self-attention has been instrumental in the success of Transformers, it
can lead to over-concentration on a few tokens during training, resulting in
suboptimal information flow. Enforcing doubly-stochastic constraints in
attention matrices has been shown to improve structure and balance in attention
distributio... |
2502.07963 | Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature? | cs.CL cs.AI | Medical research faces well-documented challenges in translating novel
treatments into clinical practice. Publishing incentives encourage researchers
to present "positive" findings, even when empirical results are equivocal.
Consequently, it is well-documented that authors often spin study results,
especially in arti... |
2502.07964 | New tools for comparing classical and neural ODE models for tumor growth | cs.LG q-bio.QM | A new computational tool TumorGrowth$.$jl for modeling tumor growth is
introduced. The tool allows the comparison of standard textbook models, such as
General Bertalanffy and Gompertz, with some newer models, including, for the
first time, neural ODE models. As an application, we revisit a human meta-study
of non-sma... |
2502.07968 | Generative Risk Minimization for Out-of-Distribution Generalization on
Graphs | cs.LG cs.AI | Out-of-distribution (OOD) generalization on graphs aims at dealing with
scenarios where the test graph distribution differs from the training graph
distributions. Compared to i.i.d. data like images, the OOD generalization
problem on graph-structured data remains challenging due to the non-i.i.d.
property and complex... |
2502.07971 | ReTreever: Tree-based Coarse-to-Fine Representations for Retrieval | cs.IR cs.AI cs.LG | Document retrieval is a core component of question-answering systems, as it
enables conditioning answer generation on new and large-scale corpora. While
effective, the standard practice of encoding documents into high-dimensional
embeddings for similarity search entails large memory and compute footprints,
and also m... |
2502.07972 | Training Sparse Mixture Of Experts Text Embedding Models | cs.CL cs.AI cs.IR | Transformer-based text embedding models have improved their performance on
benchmarks like MIRACL and BEIR by increasing their parameter counts. However,
this scaling approach introduces significant deployment challenges, including
increased inference latency and memory usage. These challenges are particularly
severe... |
2502.07974 | From Hazard Identification to Controller Design: Proactive and
LLM-Supported Safety Engineering for ML-Powered Systems | cs.SE cs.AI cs.LG | Machine learning (ML) components are increasingly integrated into software
products, yet their complexity and inherent uncertainty often lead to
unintended and hazardous consequences, both for individuals and society at
large. Despite these risks, practitioners seldom adopt proactive approaches to
anticipate and miti... |
2502.07975 | Sink equilibria and the attractors of learning in games | cs.GT cs.LG | Characterizing the limit behavior -- that is, the attractors -- of learning
dynamics is one of the most fundamental open questions in game theory. In
recent work in this front, it was conjectured that the attractors of the
replicator dynamic are in one-to-one correspondence with the sink equilibria of
the game -- the... |
2502.07977 | RESIST: Resilient Decentralized Learning Using Consensus Gradient
Descent | cs.LG math.OC stat.ML | Empirical risk minimization (ERM) is a cornerstone of modern machine learning
(ML), supported by advances in optimization theory that ensure efficient
solutions with provable algorithmic convergence rates, which measure the speed
at which optimization algorithms approach a solution, and statistical learning
rates, wh... |
2502.07978 | A Survey of In-Context Reinforcement Learning | cs.LG | Reinforcement learning (RL) agents typically optimize their policies by
performing expensive backward passes to update their network parameters.
However, some agents can solve new tasks without updating any parameters by
simply conditioning on additional context such as their action-observation
histories. This paper ... |
2502.07979 | Joint Modelling Histology and Molecular Markers for Cancer
Classification | cs.CV | Cancers are characterized by remarkable heterogeneity and diverse prognosis.
Accurate cancer classification is essential for patient stratification and
clinical decision-making. Although digital pathology has been advancing cancer
diagnosis and prognosis, the paradigm in cancer pathology has shifted from
purely relyi... |
2502.07980 | CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning
Capabilities of LLMs | cs.LG cs.AI | The role of Large Language Models (LLMs) has not been extensively explored in
analog circuit design, which could benefit from a reasoning-based approach that
transcends traditional optimization techniques. In particular, despite their
growing relevance, there are no benchmarks to assess LLMs' reasoning capability
abo... |
2502.07982 | Deep Semantic Graph Learning via LLM based Node Enhancement | cs.AI | Graph learning has attracted significant attention due to its widespread
real-world applications. Current mainstream approaches rely on text node
features and obtain initial node embeddings through shallow embedding learning
using GNNs, which shows limitations in capturing deep textual semantics. Recent
advances in L... |
2502.07985 | MetaSC: Test-Time Safety Specification Optimization for Language Models | cs.CL cs.AI | We propose a novel dynamic safety framework that optimizes language model
(LM) safety reasoning at inference time without modifying model weights.
Building on recent advances in self-critique methods, our approach leverages a
meta-critique mechanism that iteratively updates safety prompts-termed
specifications-to dri... |
2502.07987 | Universal Adversarial Attack on Aligned Multimodal LLMs | cs.AI | We propose a universal adversarial attack on multimodal Large Language Models
(LLMs) that leverages a single optimized image to override alignment safeguards
across diverse queries and even multiple models. By backpropagating through the
vision encoder and language head, we craft a synthetic image that forces the
mod... |
2502.07990 | Learning Effective Dynamics across Spatio-Temporal Scales of Complex
Flows | cs.LG physics.comp-ph physics.flu-dyn | Modeling and simulation of complex fluid flows with dynamics that span
multiple spatio-temporal scales is a fundamental challenge in many scientific
and engineering domains. Full-scale resolving simulations for systems such as
highly turbulent flows are not feasible in the foreseeable future, and
reduced-order models... |
2502.07993 | What is a Sketch-and-Precondition Derivation for Low-Rank Approximation?
Inverse Power Error or Inverse Power Estimation? | math.NA cs.CC cs.LG cs.NA stat.CO stat.ML | Randomized sketching accelerates large-scale numerical linear algebra by
reducing computational complexity. While the traditional sketch-and-solve
approach reduces the problem size directly through sketching, the
sketch-and-precondition method leverages sketching to construct a computational
friendly preconditioner. ... |
2502.07998 | Adaptive kernel predictors from feature-learning infinite limits of
neural networks | cs.LG cond-mat.dis-nn stat.ML | Previous influential work showed that infinite width limits of neural
networks in the lazy training regime are described by kernel machines. Here, we
show that neural networks trained in the rich, feature learning infinite-width
regime in two different settings are also described by kernel machines, but
with data-dep... |
2502.08001 | Unveiling Client Privacy Leakage from Public Dataset Usage in Federated
Distillation | cs.CR cs.LG | Federated Distillation (FD) has emerged as a popular federated training
framework, enabling clients to collaboratively train models without sharing
private data. Public Dataset-Assisted Federated Distillation (PDA-FD), which
leverages public datasets for knowledge sharing, has become widely adopted.
Although PDA-FD e... |
2502.08003 | Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models | cs.LG | We study a novel heterogeneous multi-agent multi-armed bandit problem with a
cluster structure induced by stochastic block models, influencing not only
graph topology, but also reward heterogeneity. Specifically, agents are
distributed on random graphs based on stochastic block models - a generalized
Erdos-Renyi mode... |
2502.08004 | Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based
Inference and Bayesian Optimal Experimental Design | stat.ML cs.LG | Simulation-based inference (SBI) is a method to perform inference on a
variety of complex scientific models with challenging inference (inverse)
problems. Bayesian Optimal Experimental Design (BOED) aims to efficiently use
experimental resources to make better inferences. Various stochastic
gradient-based BOED method... |
2502.08005 | Towards Training One-Step Diffusion Models Without Distillation | cs.LG cs.CV | Recent advances in one-step generative models typically follow a two-stage
process: first training a teacher diffusion model and then distilling it into a
one-step student model. This distillation process traditionally relies on both
the teacher model's score function to compute the distillation loss and its
weights ... |
2502.08006 | Greed is Good: Guided Generation from a Greedy Perspective | cs.LG cs.AI stat.ML | Training-free guided generation is a widely used and powerful technique that
allows the end user to exert further control over the generative process of
diffusion models. In this work, we explore the guided generation from the
perspective of optimizing the solution trajectory of a neural differential
equation in a gr... |
2502.08007 | The Role of Randomness in Stability | cs.LG stat.ML | Stability is a central property in learning and statistics promising the
output of an algorithm $A$ does not change substantially when applied to
similar datasets $S$ and $S'$. It is an elementary fact that any sufficiently
stable algorithm (e.g.\ one returning the same result with high probability,
satisfying privac... |
2502.08008 | An Interactive Framework for Implementing Privacy-Preserving Federated
Learning: Experiments on Large Language Models | cs.LG cs.CR | Federated learning (FL) enhances privacy by keeping user data on local
devices. However, emerging attacks have demonstrated that the updates shared by
users during training can reveal significant information about their data. This
has greatly thwart the adoption of FL methods for training robust AI models in
sensitiv... |
2502.08009 | The Geometry of Prompting: Unveiling Distinct Mechanisms of Task
Adaptation in Language Models | cs.CL | Decoder-only language models have the ability to dynamically switch between
various computational tasks based on input prompts. Despite many successful
applications of prompting, there is very limited understanding of the internal
mechanism behind such flexibility. In this work, we investigate how different
prompting... |
2502.08011 | Training-Free Safe Denoisers for Safe Use of Diffusion Models | cs.AI | There is growing concern over the safety of powerful diffusion models (DMs),
as they are often misused to produce inappropriate, not-safe-for-work (NSFW)
content or generate copyrighted material or data of individuals who wish to be
forgotten. Many existing methods tackle these issues by heavily relying on
text-based... |
2502.08020 | Speculate, then Collaborate: Fusing Knowledge of Language Models during
Decoding | cs.CL cs.AI | Large Language Models (LLMs) often excel in specific domains but fall short
in others due to the limitations of their training. Thus, enabling LLMs to
solve problems collaboratively by integrating their complementary knowledge
promises to improve their performance across domains. To realize this
potential, we introdu... |
2502.08021 | Model Selection for Off-policy Evaluation: New Algorithms and
Experimental Protocol | cs.LG cs.AI stat.ML | Holdout validation and hyperparameter tuning from data is a long-standing
problem in offline reinforcement learning (RL). A standard framework is to use
off-policy evaluation (OPE) methods to evaluate and select the policies, but
OPE either incurs exponential variance (e.g., importance sampling) or has
hyperparameter... |
2502.08023 | Performance Analysis of Infrastructure Sharing Techniques in Cellular
Networks: A Percolation Theory Approach | eess.SY cs.SY | In the context of 5G, infrastructure sharing has been identified as a
potential solution to reduce the investment costs of cellular networks. In
particular, it can help low-income regions build 5G networks more affordably
and further bridge the digital divide. There are two main kinds of
infrastructure sharing: passi... |
2502.08024 | Initialization Matters: Unraveling the Impact of Pre-Training on
Federated Learning | cs.LG cs.DC | Initializing with pre-trained models when learning on downstream tasks is
becoming standard practice in machine learning. Several recent works explore
the benefits of pre-trained initialization in a federated learning (FL)
setting, where the downstream training is performed at the edge clients with
heterogeneous data... |
2502.08025 | From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI
Synthesis | cs.CV | While functional magnetic resonance imaging (fMRI) offers rich spatial
resolution, it is limited by high operational costs and significant
infrastructural demands. In contrast, electroencephalography (EEG) provides
millisecond-level precision in capturing electrical activity but lacks the
spatial resolution necessary... |
2502.08026 | Contextual Subspace Manifold Projection for Structural Refinement of
Large Language Model Representations | cs.CL | Internal representations within deep neural architectures encode
high-dimensional abstractions of linguistic structures, yet they often exhibit
inefficiencies in feature distribution, limiting expressiveness and
adaptability. Contextual Subspace Manifold Projection introduces a structured
refinement technique that se... |
2502.08033 | End-to-End Predictive Planner for Autonomous Driving with Consistency
Models | cs.RO cs.LG | Trajectory prediction and planning are fundamental components for autonomous
vehicles to navigate safely and efficiently in dynamic environments.
Traditionally, these components have often been treated as separate modules,
limiting the ability to perform interactive planning and leading to
computational inefficiency ... |
2502.08037 | Franken-Adapter: Cross-Lingual Adaptation of LLMs by Embedding Surgery | cs.CL | The capabilities of Large Language Models (LLMs) in low-resource languages
lag far behind those in English, making their universal accessibility a
significant challenge. To alleviate this, we present
$\textit{Franken-Adapter}$, a modular language adaptation approach for
decoder-only LLMs with embedding surgery. Our m... |
2502.08041 | The Art of Misclassification: Too Many Classes, Not Enough Points | cs.LG cs.IT math.IT | Classification is a ubiquitous and fundamental problem in artificial
intelligence and machine learning, with extensive efforts dedicated to
developing more powerful classifiers and larger datasets. However, the
classification task is ultimately constrained by the intrinsic properties of
datasets, independently of com... |
2502.08045 | Break the Checkbox: Challenging Closed-Style Evaluations of Cultural
Alignment in LLMs | cs.CL cs.AI cs.CY | A large number of studies rely on closed-style multiple-choice surveys to
evaluate cultural alignment in Large Language Models (LLMs). In this work, we
challenge this constrained evaluation paradigm and explore more realistic,
unconstrained approaches. Using the World Values Survey (WVS) and Hofstede
Cultural Dimensi... |
2502.08047 | WorldGUI: Dynamic Testing for Comprehensive Desktop GUI Automation | cs.AI cs.MA | Current GUI agents have achieved outstanding performance in GUI element
grounding. However, planning remains highly challenging, especially due to
sensitivity to the initial state of the environment. Specifically, slight
differences in the initial state-such as the target software not being open or
the interface not ... |
2502.08054 | COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual
Occluded Grasping | cs.RO cs.LG | This paper addresses the challenge of occluded robot grasping, i.e. grasping
in situations where the desired grasp poses are kinematically infeasible due to
environmental constraints such as surface collisions. Traditional robot
manipulation approaches struggle with the complexity of non-prehensile or
bimanual strate... |
2502.08055 | SLVR: Securely Leveraging Client Validation for Robust Federated
Learning | cs.CR cs.LG | Federated Learning (FL) enables collaborative model training while keeping
client data private. However, exposing individual client updates makes FL
vulnerable to reconstruction attacks. Secure aggregation mitigates such privacy
risks but prevents the server from verifying the validity of each client
update, creating... |
2502.08056 | Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning | cs.LG cs.AI cs.MA | Today's gen-AI workflows that involve multiple ML model calls, tool/API
calls, data retrieval, or generic code execution are often tuned manually in an
ad-hoc way that is both time-consuming and error-prone. In this paper, we
propose a systematic approach for automatically tuning gen-AI workflows. Our
key insight is ... |
2502.08058 | General Coded Computing: Adversarial Settings | cs.DC cs.LG | Conventional coded computing frameworks are predominantly tailored for
structured computations, such as matrix multiplication and polynomial
evaluation. Such tasks allow the reuse of tools and techniques from algebraic
coding theory to improve the reliability of distributed systems in the presence
of stragglers and a... |
2502.08059 | On Mechanistic Circuits for Extractive Question-Answering | cs.CL cs.LG | Large language models are increasingly used to process documents and
facilitate question-answering on them. In our paper, we extract mechanistic
circuits for this real-world language modeling task: context-augmented language
modeling for extractive question-answering (QA) tasks and understand the
potential benefits o... |
2502.08063 | Multi-Agent Performative Prediction Beyond the Insensitivity Assumption:
A Case Study for Mortgage Competition | cs.GT cs.LG | Performative prediction models account for feedback loops in decision-making
processes where predictions influence future data distributions. While existing
work largely assumes insensitivity of data distributions to small strategy
changes, this assumption usually fails in real-world competitive (i.e.
multi-agent) se... |
2502.08071 | Collaborative Filtering Meets Spectrum Shift: Connecting User-Item
Interaction with Graph-Structured Side Information | cs.IR | Graph Neural Network (GNN) has demonstrated their superiority in
collaborative filtering, where the user-item (U-I) interaction bipartite graph
serves as the fundamental data format. However, when graph-structured side
information (e.g., multimodal similarity graphs or social networks) is
integrated into the U-I bipa... |
2502.08075 | Knowledge Swapping via Learning and Unlearning | cs.CV | We introduce \textbf{Knowledge Swapping}, a novel task designed to
selectively regulate knowledge of a pretrained model by enabling the forgetting
of user\-specified information, retaining essential knowledge, and acquiring
new knowledge simultaneously. By delving into the analysis of knock-on feature
hierarchy, we f... |
2502.08077 | Cascading Bandits Robust to Adversarial Corruptions | cs.LG | Online learning to rank sequentially recommends a small list of items to
users from a large candidate set and receives the users' click feedback. In
many real-world scenarios, users browse the recommended list in order and click
the first attractive item without checking the rest. Such behaviors are usually
formulate... |
2502.08079 | MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained
Models | cs.CV | Current adversarial attacks for evaluating the robustness of vision-language
pre-trained (VLP) models in multi-modal tasks suffer from limited
transferability, where attacks crafted for a specific model often struggle to
generalize effectively across different models, limiting their utility in
assessing robustness mo... |
2502.08080 | NLI under the Microscope: What Atomic Hypothesis Decomposition Reveals | cs.CL | Decomposition of text into atomic propositions is a flexible framework
allowing for the closer inspection of input and output text. We use atomic
decomposition of hypotheses in two natural language reasoning tasks,
traditional NLI and defeasible NLI, to form atomic sub-problems, or granular
inferences that models mus... |
2502.08083 | Mixture of Decoupled Message Passing Experts with Entropy Constraint for
General Node Classification | cs.LG cs.SI | The varying degrees of homophily and heterophily in real-world graphs
persistently constrain the universality of graph neural networks (GNNs) for
node classification. Adopting a data-centric perspective, this work reveals an
inherent preference of different graphs towards distinct message encoding
schemes: homophilou... |
2502.08089 | A Cooperative Bearing-Rate Approach for Observability-Enhanced Target
Motion Estimation | cs.RO cs.SY eess.SY | Vision-based target motion estimation is a fundamental problem in many
robotic tasks. The existing methods have the limitation of low observability
and, hence, face challenges in tracking highly maneuverable targets. Motivated
by the aerial target pursuit task where a target may maneuver in 3D space, this
paper studi... |
2502.08092 | GCoT: Chain-of-Thought Prompt Learning for Graphs | cs.CL cs.AI | Chain-of-thought (CoT) prompting has achieved remarkable success in natural
language processing (NLP). However, its vast potential remains largely
unexplored for graphs. This raises an interesting question: How can we design
CoT prompting for graphs to guide graph models to learn step by step? On one
hand, unlike nat... |
2502.08093 | Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity
Integration using Gaussian Process | cs.RO | Radar ensures robust sensing capabilities in adverse weather conditions, yet
challenges remain due to its high inherent noise level. Existing radar odometry
has overcome these challenges with strategies such as filtering spurious
points, exploiting Doppler velocity, or integrating with inertial measurements.
This pap... |
2502.08097 | ID-Cloak: Crafting Identity-Specific Cloaks Against Personalized
Text-to-Image Generation | cs.CV cs.CR | Personalized text-to-image models allow users to generate images of new
concepts from several reference photos, thereby leading to critical concerns
regarding civil privacy. Although several anti-personalization techniques have
been developed, these methods typically assume that defenders can afford to
design a priva... |
2502.08098 | Unsupervised categorization of similarity measures | cs.LG cs.NE | In general, objects can be distinguished on the basis of their features, such
as color or shape. In particular, it is assumed that similarity judgments about
such features can be processed independently in different metric spaces.
However, the unsupervised categorization mechanism of metric spaces
corresponding to ob... |
2502.08101 | Rethinking Tokenized Graph Transformers for Node Classification | cs.LG cs.AI | Node tokenized graph Transformers (GTs) have shown promising performance in
node classification. The generation of token sequences is the key module in
existing tokenized GTs which transforms the input graph into token sequences,
facilitating the node representation learning via Transformer. In this paper,
we observe... |
2502.08105 | Out-of-Distribution Detection on Graphs: A Survey | cs.LG | Graph machine learning has witnessed rapid growth, driving advancements
across diverse domains. However, the in-distribution assumption, where training
and testing data share the same distribution, often breaks in real-world
scenarios, leading to degraded model performance under distribution shifts.
This challenge ha... |
2502.08106 | PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced
Text-to-Image Generation | cs.LG cs.AI cs.CV stat.ML | Diffusion models have made significant advancements in recent years. However,
their performance often deteriorates when trained or fine-tuned on imbalanced
datasets. This degradation is largely due to the disproportionate
representation of majority and minority data in image-text pairs. In this
paper, we propose a ge... |
2502.08108 | Generative AI and Empirical Software Engineering: A Paradigm Shift | cs.SE cs.AI | The widespread adoption of generative AI in software engineering marks a
paradigm shift, offering new opportunities to design and utilize software
engineering tools while influencing both developers and the artifacts they
create. Traditional empirical methods in software engineering, including
quantitative, qualitati... |
2502.08109 | HuDEx: Integrating Hallucination Detection and Explainability for
Enhancing the Reliability of LLM responses | cs.CL cs.AI | Recent advances in large language models (LLMs) have shown promising
improvements, often surpassing existing methods across a wide range of
downstream tasks in natural language processing. However, these models still
face challenges, which may hinder their practical applicability. For example,
the phenomenon of hallu... |
2502.08115 | Neuromorphic Digital-Twin-based Controller for Indoor Multi-UAV Systems
Deployment | cs.NE | Presented study introduces a novel distributed cloud-edge framework for
autonomous multi-UAV systems that combines the computational efficiency of
neuromorphic computing with nature-inspired control strategies. The proposed
architecture equips each UAV with an individual Spiking Neural Network (SNN)
that learns to re... |
2502.08119 | Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for
Unmanned Surface Vehicles | cs.AI cs.RO | The increasing deployment of unmanned surface vehicles (USVs) require
computational support and coverage in applications such as maritime search and
rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial
services, and ground stations (GSs) can provide powerful supports, which can
cooperate to hel... |
2502.08122 | Hookpad Aria: A Copilot for Songwriters | cs.SD cs.AI cs.LG | We present Hookpad Aria, a generative AI system designed to assist musicians
in writing Western pop songs. Our system is seamlessly integrated into Hookpad,
a web-based editor designed for the composition of lead sheets: symbolic music
scores that describe melody and harmony. Hookpad Aria has numerous generation
capa... |
2502.08123 | Provably Robust Federated Reinforcement Learning | cs.CR cs.DC cs.LG | Federated reinforcement learning (FRL) allows agents to jointly learn a
global decision-making policy under the guidance of a central server. While FRL
has advantages, its decentralized design makes it prone to poisoning attacks.
To mitigate this, Byzantine-robust aggregation techniques tailored for FRL have
been int... |
2502.08125 | Incremental Approximate Single-Source Shortest Paths with Predictions | cs.DS cs.LG | The algorithms-with-predictions framework has been used extensively to
develop online algorithms with improved beyond-worst-case competitive ratios.
Recently, there is growing interest in leveraging predictions for designing
data structures with improved beyond-worst-case running times. In this paper,
we study the fu... |
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