id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.02167 | Multilingual Attribute Extraction from News Web Pages | cs.CL cs.IR | This paper addresses the challenge of automatically extracting attributes
from news article web pages across multiple languages. Recent neural network
models have shown high efficacy in extracting information from semi-structured
web pages. However, these models are predominantly applied to domains like
e-commerce an... |
2502.02170 | Graph Neural Networks for O-RAN Mobility Management: A Link Prediction
Approach | cs.NI cs.AI | Mobility performance has been a key focus in cellular networks up to 5G. To
enhance handover (HO) performance, 3GPP introduced Conditional Handover (CHO)
and Layer 1/Layer 2 Triggered Mobility (LTM) mechanisms in 5G. While these
reactive HO strategies address the trade-off between HO failures (HOF) and
ping-pong effe... |
2502.02171 | DeepForest: Sensing Into Self-Occluding Volumes of Vegetation With
Aerial Imaging | cs.CV eess.IV | Access to below-canopy volumetric vegetation data is crucial for
understanding ecosystem dynamics. We address the long-standing limitation of
remote sensing to penetrate deep into dense canopy layers. LiDAR and radar are
currently considered the primary options for measuring 3D vegetation
structures, while cameras ca... |
2502.02172 | EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via
Dialogue Interpretation and Saliency Cues | cs.MM cs.CV cs.HC | We present EditIQ, a completely automated framework for cinematically editing
scenes captured via a stationary, large field-of-view and high-resolution
camera. From the static camera feed, EditIQ initially generates multiple
virtual feeds, emulating a team of cameramen. These virtual camera shots termed
rushes are su... |
2502.02173 | Mass-Editing Memory with Attention in Transformers: A cross-lingual
exploration of knowledge | cs.CL cs.AI | Recent research has explored methods for updating and modifying factual
knowledge in large language models, often focusing on specific multi-layer
perceptron blocks. This study expands on this work by examining the
effectiveness of existing knowledge editing methods across languages and
delving into the role of atten... |
2502.02175 | VLA-Cache: Towards Efficient Vision-Language-Action Model via Adaptive
Token Caching in Robotic Manipulation | cs.RO cs.CV cs.LG | Vision-Language-Action (VLA) model can process instructions and visual
perception to directly generate actions as output in an end-to-end fashion due
to its strong multi-modal reasoning capabilities. While the performance of VLA
models is promising, their computational cost can be substantial. This raises
challenge f... |
2502.02179 | Deep Ensemble approach for Enhancing Brain Tumor Segmentation in
Resource-Limited Settings | eess.IV cs.CV | Segmentation of brain tumors is a critical step in treatment planning, yet
manual segmentation is both time-consuming and subjective, relying heavily on
the expertise of radiologists. In Sub-Saharan Africa, this challenge is
magnified by overburdened medical systems and limited access to advanced
imaging modalities a... |
2502.02180 | The Elicitation Game: Evaluating Capability Elicitation Techniques | cs.AI cs.LG | Capability evaluations are required to understand and regulate AI systems
that may be deployed or further developed. Therefore, it is important that
evaluations provide an accurate estimation of an AI system's capabilities.
However, in numerous cases, previously latent capabilities have been elicited
from models, som... |
2502.02182 | Sequence models for continuous cell cycle stage prediction from
brightfield images | cs.CV | Understanding cell cycle dynamics is crucial for studying biological
processes such as growth, development and disease progression. While
fluorescent protein reporters like the Fucci system allow live monitoring of
cell cycle phases, they require genetic engineering and occupy additional
fluorescence channels, limiti... |
2502.02185 | Generative Kernel Spectral Clustering | cs.LG | Modern clustering approaches often trade interpretability for performance,
particularly in deep learning-based methods. We present Generative Kernel
Spectral Clustering (GenKSC), a novel model combining kernel spectral
clustering with generative modeling to produce both well-defined clusters and
interpretable represe... |
2502.02187 | ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel
Diffusion | cs.CV cs.AI | This paper proposes ShapeShifter, a new 3D generative model that learns to
synthesize shape variations based on a single reference model. While generative
methods for 3D objects have recently attracted much attention, current
techniques often lack geometric details and/or require long training times and
large resourc... |
2502.02189 | deCIFer: Crystal Structure Prediction from Powder Diffraction Data using
Autoregressive Language Models | cs.LG | Novel materials drive progress across applications from energy storage to
electronics. Automated characterization of material structures with machine
learning methods offers a promising strategy for accelerating this key step in
material design. In this work, we introduce an autoregressive language model
that perform... |
2502.02190 | Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization | cs.NE cs.LG | Quality-Diversity has emerged as a powerful family of evolutionary algorithms
that generate diverse populations of high-performing solutions by implementing
local competition principles inspired by biological evolution. While these
algorithms successfully foster diversity and innovation, their specific
mechanisms rel... |
2502.02195 | EFKAN: A KAN-Integrated Neural Operator For Efficient Magnetotelluric
Forward Modeling | physics.geo-ph cs.LG | Magnetotelluric (MT) forward modeling is fundamental for improving the
accuracy and efficiency of MT inversion. Neural operators (NOs) have been
effectively used for rapid MT forward modeling, demonstrating their promising
performance in solving the MT forward modeling-related partial differential
equations (PDEs). P... |
2502.02196 | Exploiting Ensemble Learning for Cross-View Isolated Sign Language
Recognition | cs.CV cs.AI | In this paper, we present our solution to the Cross-View Isolated Sign
Language Recognition (CV-ISLR) challenge held at WWW 2025. CV-ISLR addresses a
critical issue in traditional Isolated Sign Language Recognition (ISLR), where
existing datasets predominantly capture sign language videos from a frontal
perspective, ... |
2502.02197 | An Efficient Local Search Approach for Polarized Community Discovery in
Signed Networks | cs.LG cs.AI cs.SI | Signed networks, where edges are labeled as positive or negative to indicate
friendly or antagonistic interactions, offer a natural framework for studying
polarization, trust, and conflict in social systems. Detecting meaningful group
structures in these networks is crucial for understanding online discourse,
politic... |
2502.02199 | When Dimensionality Hurts: The Role of LLM Embedding Compression for
Noisy Regression Tasks | cs.CL cs.CE cs.LG q-fin.CP | Large language models (LLMs) have shown remarkable success in language
modelling due to scaling laws found in model size and the hidden dimension of
the model's text representation. Yet, we demonstrate that compressed
representations of text can yield better performance in LLM-based regression
tasks. In this paper, w... |
2502.02201 | Can You Move These Over There? An LLM-based VR Mover for Supporting
Object Manipulation | cs.HC cs.AI cs.CL cs.ET | In our daily lives, we can naturally convey instructions for the spatial
manipulation of objects using words and gestures. Transposing this form of
interaction into virtual reality (VR) object manipulation can be beneficial. We
propose VR Mover, an LLM-empowered solution that can understand and interpret
the user's v... |
2502.02202 | Multi-level Supervised Contrastive Learning | cs.LG | Contrastive learning is a well-established paradigm in representation
learning. The standard framework of contrastive learning minimizes the distance
between "similar" instances and maximizes the distance between dissimilar ones
in the projection space, disregarding the various aspects of similarity that
can exist be... |
2502.02204 | Backcasting Policies in Transport Systems as an Optimal Control Problem
: An Example with Electric Vehicle Purchase Incentives | math.OC cs.SY eess.SY | This study represents a first attempt to build a backcasting methodology to
identify the optimal policy roadmaps in transport systems. Specifically, it
considers a passenger car fleet subsystem, modelling its evolution and
greenhouse gas emissions. The policy decision under consideration is the
monetary incentive to ... |
2502.02205 | From Uncertain to Safe: Conformal Fine-Tuning of Diffusion Models for
Safe PDE Control | cs.LG | The application of deep learning for partial differential equation
(PDE)-constrained control is gaining increasing attention. However, existing
methods rarely consider safety requirements crucial in real-world applications.
To address this limitation, we propose Safe Diffusion Models for PDE Control
(SafeDiffCon), wh... |
2502.02206 | Target-aware Bayesian inference via generalized thermodynamic
integration | stat.CO cs.CE stat.ME | In Bayesian inference, we are usually interested in the numerical
approximation of integrals that are posterior expectations or marginal
likelihoods (a.k.a., Bayesian evidence). In this paper, we focus on the
computation of the posterior expectation of a function $f(\x)$. We consider a
\emph{target-aware} scenario wh... |
2502.02207 | Human-Aided Trajectory Planning for Automated Vehicles through
Teleoperation and Arbitration Graphs | cs.RO cs.HC | Teleoperation enables remote human support of automated vehicles in scenarios
where the automation is not able to find an appropriate solution. Remote
assistance concepts, where operators provide discrete inputs to aid specific
automation modules like planning, is gaining interest due to its reduced
workload on the h... |
2502.02209 | On the Expressivity of Selective State-Space Layers: A Multivariate
Polynomial Approach | cs.LG | Recent advances in efficient sequence modeling have introduced selective
state-space layers, a key component of the Mamba architecture, which have
demonstrated remarkable success in a wide range of NLP and vision tasks. While
Mamba's empirical performance has matched or surpassed SoTA transformers on
such diverse ben... |
2502.02215 | InterLCM: Low-Quality Images as Intermediate States of Latent
Consistency Models for Effective Blind Face Restoration | cs.CV | Diffusion priors have been used for blind face restoration (BFR) by
fine-tuning diffusion models (DMs) on restoration datasets to recover
low-quality images. However, the naive application of DMs presents several key
limitations. (i) The diffusion prior has inferior semantic consistency (e.g.,
ID, structure and color... |
2502.02216 | Flatten Graphs as Sequences: Transformers are Scalable Graph Generators | cs.LG stat.ML | We introduce AutoGraph, a novel autoregressive framework for generating large
attributed graphs using decoder-only transformers. At the core of our approach
is a reversible "flattening" process that transforms graphs into random
sequences. By sampling and learning from these sequences, AutoGraph enables
transformers ... |
2502.02218 | Digital Fairness Algorithms for Satellite Uplink NOMA | cs.IT eess.SP math.IT | Achieving digital fairness by using NOMA is one of the more pressing issues
in modern wireless communication systems for 5G/6G networks. This is
particularly true in the case of satellite uplink systems supporting a
population of IoT wireless devices scattered in a wide coverage area. In this
scenario, the variabilit... |
2502.02221 | Bias Detection via Maximum Subgroup Discrepancy | cs.LG cs.AI stat.ML | Bias evaluation is fundamental to trustworthy AI, both in terms of checking
data quality and in terms of checking the outputs of AI systems. In testing
data quality, for example, one may study a distance of a given dataset, viewed
as a distribution, to a given ground-truth reference dataset. However,
classical metric... |
2502.02222 | Self-dual codes and LCD codes in sum-rank metric | cs.IT math.IT | Sum-rank codes are an important class of codes which can be utilized for
linear network coding, space-time coding and distributed storage. Based on the
duality theory of sum-rank codes [Byrne, Gluesing-Luerssen, Ravagnani, IEEE
TIT, 2021], it is interesting to study self-dual sum-rank codes and linear
complementary d... |
2502.02223 | SurvHive: a package to consistently access multiple survival-analysis
packages | q-bio.QM cs.LG | Survival analysis, a foundational tool for modeling time-to-event data, has
seen growing integration with machine learning (ML) approaches to handle the
complexities of censored data and time-varying risks. Despite these advances,
leveraging state-of-the-art survival models remains a challenge due to the
fragmented n... |
2502.02225 | Exploring the latent space of diffusion models directly through singular
value decomposition | cs.CV cs.AI cs.MM | Despite the groundbreaking success of diffusion models in generating
high-fidelity images, their latent space remains relatively under-explored,
even though it holds significant promise for enabling versatile and
interpretable image editing capabilities. The complicated denoising trajectory
and high dimensionality of... |
2502.02229 | A Robust Remote Photoplethysmography Method | cs.CV | Remote photoplethysmography (rPPG) is a method for measuring a subjects heart
rate remotely using a camera. Factors such as subject movement, ambient light
level, makeup etc. complicate such measurements by distorting the observed
pulse. Recent works on this topic have proposed a variety of approaches for
accurately ... |
2502.02232 | Combinatorial Optimization Perspective based Framework for
Multi-behavior Recommendation | cs.IR | In real-world recommendation scenarios, users engage with items through
various types of behaviors. Leveraging diversified user behavior information
for learning can enhance the recommendation of target behaviors (e.g., buy), as
demonstrated by recent multi-behavior methods. The mainstream multi-behavior
recommendati... |
2502.02233 | Variance-Adjusted Cosine Distance as Similarity Metric | stat.ML cs.LG | Cosine similarity is a popular distance measure that measures the similarity
between two vectors in the inner product space. It is widely used in many data
classification algorithms like K-Nearest Neighbors, Clustering etc. This study
demonstrates limitations of application of cosine similarity. Particularly,
this st... |
2502.02234 | Mask-informed Deep Contrastive Incomplete Multi-view Clustering | cs.CV cs.LG | Multi-view clustering (MvC) utilizes information from multiple views to
uncover the underlying structures of data. Despite significant advancements in
MvC, mitigating the impact of missing samples in specific views on the
integration of knowledge from different views remains a critical challenge.
This paper proposes ... |
2502.02238 | Using ChatGPT to refine draft conceptual schemata in supply-driven
design of multidimensional cubes | cs.DB cs.SE | Refinement is a critical step in supply-driven conceptual design of
multidimensional cubes because it can hardly be automated. In fact, it includes
steps such as the labeling of attributes as descriptive and the removal of
uninteresting attributes, thus relying on the end-users' requirements on the
one hand, and on t... |
2502.02247 | Rotation-Adaptive Point Cloud Domain Generalization via Intricate
Orientation Learning | cs.CV cs.AI cs.LG | The vulnerability of 3D point cloud analysis to unpredictable rotations poses
an open yet challenging problem: orientation-aware 3D domain generalization.
Cross-domain robustness and adaptability of 3D representations are crucial but
not easily achieved through rotation augmentation. Motivated by the inherent
advanta... |
2502.02249 | Conversation AI Dialog for Medicare powered by Finetuning and Retrieval
Augmented Generation | cs.CL cs.AI | Large language models (LLMs) have shown impressive capabilities in natural
language processing tasks, including dialogue generation. This research aims to
conduct a novel comparative analysis of two prominent techniques, fine-tuning
with LoRA (Low-Rank Adaptation) and the Retrieval-Augmented Generation (RAG)
framewor... |
2502.02257 | UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic
Segmentation | cs.CV | Pre-training techniques significantly enhance the performance of semantic
segmentation tasks with limited training data. However, the efficacy under a
large domain gap between pre-training (e.g. RGB) and fine-tuning (e.g.
infrared) remains underexplored. In this study, we first benchmark the infrared
semantic segment... |
2502.02260 | Adversarial ML Problems Are Getting Harder to Solve and to Evaluate | cs.LG cs.CR | In the past decade, considerable research effort has been devoted to securing
machine learning (ML) models that operate in adversarial settings. Yet,
progress has been slow even for simple "toy" problems (e.g., robustness to
small adversarial perturbations) and is often hindered by non-rigorous
evaluations. Today, ad... |
2502.02265 | Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement
Learning Control | cs.LG cs.AI | High-precision control tasks present substantial challenges for reinforcement
learning (RL) algorithms, frequently resulting in suboptimal performance
attributed to network approximation inaccuracies and inadequate sample
quality.These issues are exacerbated when the task requires the agent to
achieve a precise goal ... |
2502.02269 | Survey of Quantization Techniques for On-Device Vision-based Crack
Detection | cs.CV cs.LG | Structural Health Monitoring (SHM) ensures the safety and longevity of
infrastructure by enabling timely damage detection. Vision-based crack
detection, combined with UAVs, addresses the limitations of traditional
sensor-based SHM methods but requires the deployment of efficient deep learning
models on resource-const... |
2502.02270 | Exact Sequence Classification with Hardmax Transformers | cs.LG math.OC stat.ML | We prove that hardmax attention transformers perfectly classify datasets of
$N$ labeled sequences in $\mathbb{R}^d$, $d\geq 2$. Specifically, given $N$
sequences with an arbitrary but finite length in $\mathbb{R}^d$, we construct a
transformer with $\mathcal{O}(N)$ blocks and $\mathcal{O}(Nd)$ parameters
perfectly cl... |
2502.02275 | A User's Guide to Sampling Strategies for Sliced Optimal Transport | cs.LG math.PR | This paper serves as a user's guide to sampling strategies for sliced optimal
transport. We provide reminders and additional regularity results on the Sliced
Wasserstein distance. We detail the construction methods, generation time
complexity, theoretical guarantees, and conditions for each strategy.
Additionally, we... |
2502.02277 | Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced
Regression | cs.LG cs.AI | In real-world regression tasks, datasets frequently exhibit imbalanced
distributions, characterized by a scarcity of data in high-complexity regions
and an abundance in low-complexity areas. This imbalance presents significant
challenges for existing classification methods with clear class boundaries,
while highlight... |
2502.02279 | A Revisit of Total Correlation in Disentangled Variational Auto-Encoder
with Partial Disentanglement | cs.LG q-bio.NC | A fully disentangled variational auto-encoder (VAE) aims to identify
disentangled latent components from observations. However, enforcing full
independence between all latent components may be too strict for certain
datasets. In some cases, multiple factors may be entangled together in a
non-separable manner, or a si... |
2502.02283 | GP-GS: Gaussian Processes for Enhanced Gaussian Splatting | cs.CV cs.AI | 3D Gaussian Splatting has emerged as an efficient photorealistic novel view
synthesis method. However, its reliance on sparse Structure-from-Motion (SfM)
point clouds consistently compromises the scene reconstruction quality. To
address these limitations, this paper proposes a novel 3D reconstruction
framework Gaussi... |
2502.02287 | Adaptive Resource Allocation Optimization Using Large Language Models in
Dynamic Wireless Environments | eess.SY cs.LG cs.SY | Deep learning (DL) has made notable progress in addressing complex radio
access network control challenges that conventional analytic methods have
struggled to solve. However, DL has shown limitations in solving constrained
NP-hard problems often encountered in network optimization, such as those
involving quality of... |
2502.02289 | Evalita-LLM: Benchmarking Large Language Models on Italian | cs.CL | We describe Evalita-LLM, a new benchmark designed to evaluate Large Language
Models (LLMs) on Italian tasks. The distinguishing and innovative features of
Evalita-LLM are the following: (i) all tasks are native Italian, avoiding
issues of translating from Italian and potential cultural biases; (ii) in
addition to wel... |
2502.02290 | FRAUD-RLA: A new reinforcement learning adversarial attack against
credit card fraud detection | cs.LG cs.AI | Adversarial attacks pose a significant threat to data-driven systems, and
researchers have spent considerable resources studying them. Despite its
economic relevance, this trend largely overlooked the issue of credit card
fraud detection. To address this gap, we propose a new threat model that
demonstrates the limita... |
2502.02295 | Intelligent Reflecting Surface Based Localization of Mixed Near-Field
and Far-Field Targets | eess.SP cs.IT math.IT | This paper considers an intelligent reflecting surface (IRS)-assisted
bi-static localization architecture for the sixth-generation (6G) integrated
sensing and communication (ISAC) network. The system consists of a transmit
user, a receive base station (BS), an IRS, and multiple targets in either the
far-field or near... |
2502.02300 | Density Ratio Estimation with Conditional Probability Paths | cs.LG | Density ratio estimation in high dimensions can be reframed as integrating a
certain quantity, the time score, over probability paths which interpolate
between the two densities. In practice, the time score has to be estimated
based on samples from the two densities. However, existing methods for this
problem remain ... |
2502.02302 | EdgeGFL: Rethinking Edge Information in Graph Feature Preference
Learning | cs.LG cs.AI | Graph Neural Networks (GNNs) have significant advantages in handling
non-Euclidean data and have been widely applied across various areas, thus
receiving increasing attention in recent years. The framework of GNN models
mainly includes the information propagation phase and the aggregation phase,
treating nodes and ed... |
2502.02304 | Comparative Analysis of FPGA and GPU Performance for Machine
Learning-Based Track Reconstruction at LHCb | hep-ex cs.DC cs.LG physics.ins-det | In high-energy physics, the increasing luminosity and detector granularity at
the Large Hadron Collider are driving the need for more efficient data
processing solutions. Machine Learning has emerged as a promising tool for
reconstructing charged particle tracks, due to its potentially linear
computational scaling wi... |
2502.02305 | Information-Theoretic Proofs for Diffusion Sampling | stat.ML cs.IT cs.LG math.IT | This paper provides an elementary, self-contained analysis of diffusion-based
sampling methods for generative modeling. In contrast to existing approaches
that rely on continuous-time processes and then discretize, our treatment works
directly with discrete-time stochastic processes and yields precise
non-asymptotic ... |
2502.02307 | UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training | cs.CV | Despite decades of research on data collection and model architectures,
current gaze estimation models face significant challenges in generalizing
across diverse data domains. While recent advances in self-supervised
pre-training have shown remarkable potential for improving model generalization
in various vision tas... |
2502.02308 | Real-Time Operator Takeover for Visuomotor Diffusion Policy Training | cs.RO cs.LG | We present a Real-Time Operator Takeover (RTOT) paradigm enabling operators
to seamlessly take control of a live visuomotor diffusion policy, guiding the
system back into desirable states or reinforcing specific demonstrations. We
present new insights in using the Mahalonobis distance to automatically
identify undesi... |
2502.02309 | Review of Demographic Bias in Face Recognition | cs.CV cs.CR | Demographic bias in face recognition (FR) has emerged as a critical area of
research, given its impact on fairness, equity, and reliability across diverse
applications. As FR technologies are increasingly deployed globally,
disparities in performance across demographic groups -- such as race,
ethnicity, and gender --... |
2502.02310 | Gaussian processes for dynamics learning in model predictive control | eess.SY cs.SY | Due to its state-of-the-art estimation performance complemented by rigorous
and non-conservative uncertainty bounds, Gaussian process regression is a
popular tool for enhancing dynamical system models and coping with their
inaccuracies. This has enabled a plethora of successful implementations of
Gaussian process-bas... |
2502.02311 | MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task
Allocation for Autonomous Vehicles with Deep Reinforcement Learning | cs.RO cs.LG cs.MA | This paper addresses the challenge of decentralized task allocation within
heterogeneous multi-agent systems operating under communication constraints. We
introduce a novel framework that integrates graph neural networks (GNNs) with a
centralized training and decentralized execution (CTDE) paradigm, further
enhanced ... |
2502.02315 | VaiBot: Shuttle Between the Instructions and Parameters of Large
Language Models | cs.LG cs.CL | How to interact with LLMs through \emph{instructions} has been widely studied
by researchers. However, previous studies have treated the emergence of
instructions and the training of LLMs on task data as separate processes,
overlooking the inherent unity between the two. This paper proposes a neural
network framework... |
2502.02316 | DIME:Diffusion-Based Maximum Entropy Reinforcement Learning | cs.LG | Maximum entropy reinforcement learning (MaxEnt-RL) has become the standard
approach to RL due to its beneficial exploration properties. Traditionally,
policies are parameterized using Gaussian distributions, which significantly
limits their representational capacity. Diffusion-based policies offer a more
expressive a... |
2502.02322 | Improving Generalization Ability for 3D Object Detection by Learning
Sparsity-invariant Features | cs.CV cs.RO | In autonomous driving, 3D object detection is essential for accurately
identifying and tracking objects. Despite the continuous development of various
technologies for this task, a significant drawback is observed in most of
them-they experience substantial performance degradation when detecting objects
in unseen dom... |
2502.02323 | Hybrid Resolver Model Generalization for Fault Condition Modeling: A
Promising Tool for Reliability Study | eess.SY cs.SY | Resolvers, like all electromagnetic devices, are constantly under
investigation, both operationally and structurally. In this regard, proposing a
modeling methodology that can save significant time without compromising
accuracy is a big honor. In this study, a generalized hybrid model is suggested
that, in addition t... |
2502.02327 | Policy-Guided Causal State Representation for Offline Reinforcement
Learning Recommendation | cs.IR cs.LG | In offline reinforcement learning-based recommender systems (RLRS), learning
effective state representations is crucial for capturing user preferences that
directly impact long-term rewards. However, raw state representations often
contain high-dimensional, noisy information and components that are not
causally relev... |
2502.02329 | ReSpark: Leveraging Previous Data Reports as References to Generate New
Reports with LLMs | cs.HC cs.CL | Creating data reports is time-consuming, as it requires iterative exploration
and understanding of data, followed by summarizing the insights. While large
language models (LLMs) are powerful tools for data processing and text
generation, they often struggle to produce complete data reports that fully
meet user expect... |
2502.02331 | On the Impact of Performative Risk Minimization for Binary Random
Variables | stat.ML cs.LG | Performativity, the phenomenon where outcomes are influenced by predictions,
is particularly prevalent in social contexts where individuals strategically
respond to a deployed model. In order to preserve the high accuracy of machine
learning models under distribution shifts caused by performativity, Perdomo et
al. (2... |
2502.02332 | Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement
Learning | math.OC cs.LG | We study task selection to enhance sample efficiency in model-agnostic
meta-reinforcement learning (MAML-RL). Traditional meta-RL typically assumes
that all available tasks are equally important, which can lead to task
redundancy when they share significant similarities. To address this, we
propose a coreset-based ta... |
2502.02334 | Event-aided Semantic Scene Completion | cs.CV cs.RO eess.IV | Autonomous driving systems rely on robust 3D scene understanding. Recent
advances in Semantic Scene Completion (SSC) for autonomous driving underscore
the limitations of RGB-based approaches, which struggle under motion blur, poor
lighting, and adverse weather. Event cameras, offering high dynamic range and
low laten... |
2502.02336 | Identifying Large-Scale Linear Parameter Varying Systems with Dynamic
Mode Decomposition Methods | eess.SY cs.LG cs.SY | Linear Parameter Varying (LPV) Systems are a well-established class of
nonlinear systems with a rich theory for stability analysis, control, and
analytical response finding, among other aspects. Although there are works on
data-driven identification of such systems, the literature is quite scarce in
terms of works th... |
2502.02338 | Geometric Neural Process Fields | cs.CV cs.LG | This paper addresses the challenge of Neural Field (NeF) generalization,
where models must efficiently adapt to new signals given only a few
observations. To tackle this, we propose Geometric Neural Process Fields
(G-NPF), a probabilistic framework for neural radiance fields that explicitly
captures uncertainty. We f... |
2502.02339 | Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking | cs.CL | Multimodal large language models (MLLMs) exhibit impressive capabilities but
still face challenges in complex visual reasoning. While recent efforts attempt
to enhance MLLMs' reasoning by incorporating OpenAI o1-like structured thinking
through explicit search structures or teacher-guided distillation, they often
str... |
2502.02340 | Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical
Segmentation | cs.CV | How to mitigate negative transfer in transfer learning is a long-standing and
challenging issue, especially in the application of medical image segmentation.
Existing methods for reducing negative transfer focus on classification or
regression tasks, ignoring the non-uniform negative transfer risk in different
image ... |
2502.02341 | Test Time Training for 4D Medical Image Interpolation | eess.IV cs.AI cs.CV | 4D medical image interpolation is essential for improving temporal resolution
and diagnostic precision in clinical applications. Previous works ignore the
problem of distribution shifts, resulting in poor generalization under
different distribution. A natural solution would be to adapt the model to a new
test distrib... |
2502.02345 | Optimal Subspace Inference for the Laplace Approximation of Bayesian
Neural Networks | cs.LG | Subspace inference for neural networks assumes that a subspace of their
parameter space suffices to produce a reliable uncertainty quantification. In
this work, we mathematically derive the optimal subspace model to a Bayesian
inference scenario based on the Laplace approximation. We demonstrate
empirically that, in ... |
2502.02347 | Exponentially Stable Combined Adaptive Control under Finite Excitation
Condition | eess.SY cs.SY | The parameter convergence relies on a stringent persistent excitation (PE)
condition in adaptive control. Several works have proposed a memory term in the
last decade to translate the PE condition to a feasible finite excitation (FE)
condition. This work proposes a combined model reference adaptive control for a
clas... |
2502.02351 | Exploring the Feasibility of AI-Assisted Spine MRI Protocol Optimization
Using DICOM Image Metadata | cs.LG | Artificial intelligence (AI) is increasingly being utilized to optimize
magnetic resonance imaging (MRI) protocols. Given that image details are
critical for diagnostic accuracy, optimizing MRI acquisition protocols is
essential for enhancing image quality. While medical physicists are responsible
for this optimizati... |
2502.02356 | A Fast Decoding Algorithm for Generalized Reed-Solomon Codes and
Alternant Codes | cs.IT math.IT | In this paper, it is shown that the syndromes of generalized Reed-Solomon
(GRS) codes and alternant codes can be characterized in terms of inverse fast
Fourier transform, regardless of code definitions. Then a fast decoding
algorithm is proposed, which has a computational complexity of $O(n\log(n-k) +
(n-k)\log^2(n-k... |
2502.02357 | Graph-based Impact Analysis of Cyber-Attacks on Behind-the-Meter
Infrastructure | eess.SY cs.SY | Behind-the-Meter assets are getting more interconnected to realise new
applications like flexible tariffs. Cyber-attacks on the resulting control
infrastructure may impact a large number of devices, which can result in severe
impact on the power system. To analyse the possible impact of such attacks we
developed a gr... |
2502.02358 | MotionLab: Unified Human Motion Generation and Editing via the
Motion-Condition-Motion Paradigm | cs.CV | Human motion generation and editing are key components of computer graphics
and vision. However, current approaches in this field tend to offer isolated
solutions tailored to specific tasks, which can be inefficient and impractical
for real-world applications. While some efforts have aimed to unify
motion-related tas... |
2502.02362 | Premise-Augmented Reasoning Chains Improve Error Identification in Math
reasoning with LLMs | cs.CL | Chain-of-Thought (CoT) prompting enhances mathematical reasoning in large
language models (LLMs) by enabling detailed step-by-step solutions. However,
due to the verbosity of LLMs, the resulting reasoning chains can be long,
making it harder to verify the reasoning steps and trace issues resulting from
dependencies b... |
2502.02363 | FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference | stat.ML cs.LG | Prediction-powered inference (PPI) enables valid statistical inference by
combining experimental data with machine learning predictions. When a
sufficient number of high-quality predictions is available, PPI results in more
accurate estimates and tighter confidence intervals than traditional methods.
In this paper, w... |
2502.02365 | Measuring social mobility in temporal networks | cs.SI physics.soc-ph | In complex networks, the rich-get-richer effect (nodes with high degree at
one point in time gain more degree in their future) is commonly observed. In
practice this is often studied on a static network snapshot, for example, a
preferential attachment model assumed to explain the more highly connected
nodes or a rich... |
2502.02367 | Field Matching: an Electrostatic Paradigm to Generate and Transfer Data | cs.LG cs.AI cs.CV | We propose Electrostatic Field Matching (EFM), a novel method that is
suitable for both generative modeling and distribution transfer tasks. Our
approach is inspired by the physics of an electrical capacitor. We place source
and target distributions on the capacitor plates and assign them positive and
negative charge... |
2502.02368 | Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in
Real-World Projects | cs.SE cs.AI | Large Language Models (LLMs) have gained attention for addressing coding
problems, but their effectiveness in fixing code maintainability remains
unclear. This study evaluates LLMs capability to resolve 127 maintainability
issues from 10 GitHub repositories. We use zero-shot prompting for Copilot Chat
and Llama 3.1, ... |
2502.02371 | Accurate Pocket Identification for Binding-Site-Agnostic Docking | q-bio.BM cs.AI cs.LG physics.bio-ph physics.med-ph | Accurate identification of druggable pockets is essential for structure-based
drug design. However, most pocket-identification algorithms prioritize their
geometric properties over downstream docking performance. To address this
limitation, we developed RAPID-Net, a pocket-finding algorithm for seamless
integration w... |
2502.02372 | MaintaAvatar: A Maintainable Avatar Based on Neural Radiance Fields by
Continual Learning | cs.CV cs.AI | The generation of a virtual digital avatar is a crucial research topic in the
field of computer vision. Many existing works utilize Neural Radiance Fields
(NeRF) to address this issue and have achieved impressive results. However,
previous works assume the images of the training person are available and fixed
while t... |
2502.02377 | A Minimax Approach to Ad Hoc Teamwork | cs.AI | We propose a minimax-Bayes approach to Ad Hoc Teamwork (AHT) that optimizes
policies against an adversarial prior over partners, explicitly accounting for
uncertainty about partners at time of deployment. Unlike existing methods that
assume a specific distribution over partners, our approach improves worst-case
perfo... |
2502.02379 | No Metric to Rule Them All: Toward Principled Evaluations of
Graph-Learning Datasets | cs.LG cs.SI stat.ML | Benchmark datasets have proved pivotal to the success of graph learning, and
good benchmark datasets are crucial to guide the development of the field.
Recent research has highlighted problems with graph-learning datasets and
benchmarking practices -- revealing, for example, that methods which ignore the
graph struct... |
2502.02380 | The Cost Perspective of Liquid Democracy: Feasibility and Control | cs.GT cs.AI | We examine an approval-based model of Liquid Democracy with a budget
constraint on voting and delegating costs, aiming to centrally select casting
voters ensuring complete representation of the electorate. From a computational
complexity perspective, we focus on minimizing overall costs, maintaining short
delegation ... |
2502.02382 | Circular Microalgae-Based Carbon Control for Net Zero | math.DS cs.LG math.OC | The alteration of the climate in various areas of the world is of increasing
concern since climate stability is a necessary condition for human survival as
well as every living organism. The main reason of climate change is the
greenhouse effect caused by the accumulation of carbon dioxide in the
atmosphere. In this ... |
2502.02384 | STAIR: Improving Safety Alignment with Introspective Reasoning | cs.CL | Ensuring the safety and harmlessness of Large Language Models (LLMs) has
become equally critical as their performance in applications. However, existing
safety alignment methods typically suffer from safety-performance trade-offs
and the susceptibility to jailbreak attacks, primarily due to their reliance on
direct r... |
2502.02385 | Achieving Hiding and Smart Anti-Jamming Communication: A Parallel DRL
Approach against Moving Reactive Jammer | cs.IT cs.LG cs.SY eess.SY math.IT | This paper addresses the challenge of anti-jamming in moving reactive jamming
scenarios. The moving reactive jammer initiates high-power tracking jamming
upon detecting any transmission activity, and when unable to detect a signal,
resorts to indiscriminate jamming. This presents dual imperatives: maintaining
hiding ... |
2502.02386 | Hypergraph Link Prediction via Hyperedge Copying | cs.SI nlin.AO physics.data-an physics.soc-ph | We propose a generative model of temporally-evolving hypergraphs in which
hyperedges form via noisy copying of previous hyperedges. Our proposed model
reproduces several stylized facts from many empirical hypergraphs, is learnable
from data, and defines a likelihood over a complete hypergraph rather than
ego-based or... |
2502.02389 | Rate-reliability functions for deterministic identification | cs.IT math.IT quant-ph | We investigate deterministic identification over arbitrary memoryless
channels under the constraint that the error probabilities of first and second
kind are exponentially small in the block length $n$, controlled by reliability
exponents $E_1,E_2 \geq 0$. In contrast to the regime of slowly vanishing
errors, where t... |
2502.02390 | CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large
Language Models Reasoning | cs.CL cs.AI | Research on LLM technologies is rapidly emerging, with most of them employing
a 'fast thinking' approach to inference. Most LLMs generate the final result
based solely on a single query and LLM's reasoning capabilities. However, with
the advent of OpenAI-o1, 'slow thinking' techniques have garnered increasing
attenti... |
2502.02391 | FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named
Entity Recognition in a Multilingual Framework | cs.CL cs.AI | We introduce FewTopNER, a novel framework that integrates few-shot named
entity recognition (NER) with topic-aware contextual modeling to address the
challenges of cross-lingual and low-resource scenarios. FewTopNER leverages a
shared multilingual encoder based on XLM-RoBERTa, augmented with
language-specific calibra... |
2502.02393 | Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention
Transformers | cs.LG cs.CC | Chain-of-thought reasoning and scratchpads have emerged as critical tools for
enhancing the computational capabilities of transformers. While theoretical
results show that polynomial-length scratchpads can extend transformers'
expressivity from $TC^0$ to $PTIME$, their required length remains poorly
understood. Empir... |
2502.02394 | Robust contraction-based model predictive control for nonlinear systems | eess.SY cs.SY | Model Predictive Control (MPC) is a widely known control method that has
proved to be particularly effective in multivariable and constrained control.
Closed-loop stability and recursive feasibility can be guaranteed by employing
accurate models in prediction and suitable terminal ingredients, i.e. the
terminal cost ... |
2502.02406 | LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in
Multimodal Large Language Models | cs.CV cs.AI cs.DC cs.LG | Cross-attention is commonly adopted in multimodal large language models
(MLLMs) for integrating visual information into the language backbone. However,
in applications with large visual inputs, such as video understanding,
processing a large number of visual tokens in cross-attention layers leads to
high memory deman... |
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