id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.01107 | GTG: Generalizable Trajectory Generation Model for Urban Mobility | [
"cs.LG"
] | Trajectory data mining is crucial for smart city management. However, collecting large-scale trajectory datasets is challenging due to factors such as commercial conflicts and privacy regulations. Therefore, we urgently need trajectory generation techniques to address this issue. Existing trajectory generation methods ... | {
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2502.01108 | Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for
Wearable Applications Across Lab and Field Settings | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Photoplethysmography (PPG)-based foundation models are gaining traction due to the widespread use of PPG in biosignal monitoring and their potential to generalize across diverse health applications. In this paper, we introduce Pulse-PPG, the first open-source PPG foundation model trained exclusively on raw PPG data col... | {
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2502.01111 | A generative foundation model for an all-in-one seismic processing
framework | [
"physics.geo-ph",
"cs.AI"
] | Seismic data often face challenges in their utilization due to noise contamination, incomplete acquisition, and limited low-frequency information, which hinder accurate subsurface imaging and interpretation. Traditional processing methods rely heavily on task-specific designs to address these challenges and fail to acc... | {
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2502.01113 | GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation | [
"cs.IR",
"cs.AI",
"cs.CL"
] | Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relationships between pieces of knowledge, limiting their performance in intricate reasoning that requires integrating knowledge from multiple sour... | {
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2502.01116 | Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment
after Instruction Tuning | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have emerged as powerful tools for addressing a wide range of general inquiries and tasks. Despite this, fine-tuning aligned LLMs on smaller, domain-specific datasets, critical to adapting them to specialized tasks, can inadvertently degrade their safety alignment, even when the datasets ar... | {
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2502.01117 | Learning to Learn Weight Generation via Trajectory Diffusion | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Diffusion-based algorithms have emerged as promising techniques for weight generation, particularly in scenarios like multi-task learning that require frequent weight updates. However, existing solutions suffer from limited cross-task transferability. In addition, they only utilize optimal weights as training samples, ... | {
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2502.01118 | Large Language Model-Enhanced Multi-Armed Bandits | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs) have been adopted to solve sequential decision-making tasks such as multi-armed bandits (MAB), in which an LLM is directly instructed to select the arms to pull in every iteration. However, this paradigm of direct arm selection using LLMs has been shown to be suboptimal in many MAB tasks. T... | {
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2502.01122 | Learning Efficient Positional Encodings with Graph Neural Networks | [
"cs.LG"
] | Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity of Graph Neural Networks (GNNs). However, designing powerful and efficient PEs for graphs poses sign... | {
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2502.01126 | Language Models Prefer What They Know: Relative Confidence Estimation
via Confidence Preferences | [
"cs.CL"
] | Language models (LMs) should provide reliable confidence estimates to help users detect mistakes in their outputs and defer to human experts when necessary. Asking a language model to assess its confidence ("Score your confidence from 0-1.") is a natural way of evaluating its uncertainty. However, models struggle to pr... | {
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2502.01127 | The Battling Influencers Game: Nash Equilibria Structure of a Potential
Game and Implications to Value Alignment | [
"cs.GT",
"cs.AI"
] | When multiple influencers attempt to compete for a receiver's attention, their influencing strategies must account for the presence of one another. We introduce the Battling Influencers Game (BIG), a multi-player simultaneous-move general-sum game, to provide a game-theoretic characterization of this social phenomenon.... | {
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2502.01128 | C codegen considered unnecessary: go directly to binary, do not pass C.
Compilation of Julia code for deployment in model-based engineering | [
"eess.SY",
"cs.SY"
] | Since time immemorial an old adage has always seemed to ring true: you cannot use a high-level productive programming language like Python or R for real-time control and embedded-systems programming, you must rewrite your program in C. We present a counterexample to this mantra by demonstrating how recent compiler deve... | {
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2502.01129 | Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless
Networks | [
"cs.DC",
"cs.AI",
"cs.ET",
"cs.LG"
] | This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Networ... | {
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2502.01131 | Simple Linear Neuron Boosting | [
"cs.LG",
"stat.ML"
] | Given a differentiable network architecture and loss function, we revisit optimizing the network's neurons in function space using Boosted Backpropagation (Grubb & Bagnell, 2010), in contrast to optimizing in parameter space. From this perspective, we reduce descent in the space of linear functions that optimizes the n... | {
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2502.01137 | Self-Organizing Interaction Spaces: A Framework for Engineering
Pervasive Applications in Mobile and Distributed Environments | [
"cs.DC",
"cs.AI",
"cs.LG",
"cs.SE"
] | The rapid adoption of pervasive and mobile computing has led to an unprecedented rate of data production and consumption by mobile applications at the network edge. These applications often require interactions such as data exchange, behavior coordination, and collaboration, which are typically mediated by cloud server... | {
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2502.01141 | Beyond Yes or No: Predictive Compliance Monitoring Approaches for
Quantifying the Magnitude of Compliance Violations | [
"cs.LG",
"cs.AI"
] | Most existing process compliance monitoring approaches detect compliance violations in an ex post manner. Only predicate prediction focuses on predicting them. However, predicate prediction provides a binary yes/no notion of compliance, lacking the ability to measure to which extent an ongoing process instance deviates... | {
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2502.01142 | DeepRAG: Thinking to Retrieval Step by Step for Large Language Models | [
"cs.AI",
"cs.CL",
"cs.IR"
] | Large Language Models (LLMs) have shown remarkable potential in reasoning while they still suffer from severe factual hallucinations due to timeliness, accuracy, and coverage of parametric knowledge. Meanwhile, integrating reasoning with retrieval-augmented generation (RAG) remains challenging due to ineffective task d... | {
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2502.01143 | ASAP: Aligning Simulation and Real-World Physics for Learning Agile
Humanoid Whole-Body Skills | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a significant challenge due to the dynamics mismatch between simulation and the real world. Existing approaches, such as system identification ... | {
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2502.01145 | Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task
Learning: A Sheaf-Theoretic Approach | [
"cs.LG"
] | Federated multi-task learning (FMTL) aims to simultaneously learn multiple related tasks across clients without sharing sensitive raw data. However, in the decentralized setting, existing FMTL frameworks are limited in their ability to capture complex task relationships and handle feature and sample heterogeneity acros... | {
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2502.01146 | Quantum Machine Learning: A Hands-on Tutorial for Machine Learning
Practitioners and Researchers | [
"quant-ph",
"cs.AI",
"cs.LG"
] | This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine learning. For self-consistency, this tutorial covers foundational principles, representative QML algo... | {
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2502.01152 | Gradient Norm-based Fine-Tuning for Backdoor Defense in Automatic Speech
Recognition | [
"cs.SD",
"cs.LG",
"eess.AS"
] | Backdoor attacks have posed a significant threat to the security of deep neural networks (DNNs). Despite considerable strides in developing defenses against backdoor attacks in the visual domain, the specialized defenses for the audio domain remain empty. Furthermore, the defenses adapted from the visual to audio domai... | {
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2502.01154 | Jailbreaking with Universal Multi-Prompts | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | Large language models (LLMs) have seen rapid development in recent years, revolutionizing various applications and significantly enhancing convenience and productivity. However, alongside their impressive capabilities, ethical concerns and new types of attacks, such as jailbreaking, have emerged. While most prompting t... | {
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2502.01156 | On the impact of the parametrization of deep convolutional neural
networks on post-training quantization | [
"cs.IT",
"math.IT"
] | This paper introduces novel theoretical approximation bounds for the output of quantized neural networks, with a focus on convolutional neural networks (CNN). By considering layerwise parametrization and focusing on the quantization of weights, we provide bounds that gain several orders of magnitude compared to state-o... | {
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2502.01157 | Radiant Foam: Real-Time Differentiable Ray Tracing | [
"cs.CV"
] | Research on differentiable scene representations is consistently moving towards more efficient, real-time models. Recently, this has led to the popularization of splatting methods, which eschew the traditional ray-based rendering of radiance fields in favor of rasterization. This has yielded a significant improvement i... | {
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2502.01158 | MIND: Modality-Informed Knowledge Distillation Framework for Multimodal
Clinical Prediction Tasks | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Multimodal fusion leverages information across modalities to learn better feature representations with the goal of improving performance in fusion-based tasks. However, multimodal datasets, especially in medical settings, are typically smaller than their unimodal counterparts, which can impede the performance of multim... | {
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2502.01159 | AtmosSci-Bench: Evaluating the Recent Advance of Large Language Model
for Atmospheric Science | [
"cs.LG",
"cs.AI"
] | The rapid advancements in large language models (LLMs), particularly in their reasoning capabilities, hold transformative potential for addressing complex challenges in atmospheric science. However, leveraging LLMs effectively in this domain requires a robust and comprehensive evaluation benchmark. To address this need... | {
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2502.01160 | Scalable Precise Computation of Shannon Entropy | [
"cs.AI",
"cs.IT",
"math.IT"
] | Quantitative information flow analyses (QIF) are a class of techniques for measuring the amount of confidential information leaked by a program to its public outputs. Shannon entropy is an important method to quantify the amount of leakage in QIF. This paper focuses on the programs modeled in Boolean constraints an... | {
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2502.01167 | ConditionNET: Learning Preconditions and Effects for Execution
Monitoring | [
"cs.RO",
"cs.LG"
] | The introduction of robots into everyday scenarios necessitates algorithms capable of monitoring the execution of tasks. In this paper, we propose ConditionNET, an approach for learning the preconditions and effects of actions in a fully data-driven manner. We develop an efficient vision-language model and introduce ad... | {
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2502.01170 | Label Distribution Learning with Biased Annotations by Learning
Multi-Label Representation | [
"cs.LG"
] | Multi-label learning (MLL) has gained attention for its ability to represent real-world data. Label Distribution Learning (LDL), an extension of MLL to learning from label distributions, faces challenges in collecting accurate label distributions. To address the issue of biased annotations, based on the low-rank assump... | {
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2502.01171 | Efficient and Scalable Density Functional Theory Hamiltonian Prediction
through Adaptive Sparsity | [
"cs.LG",
"physics.comp-ph"
] | Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph neural networks have achieved remarkable success in this domain, their substantial computational cost-driven by high-order tensor product (TP... | {
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2502.01172 | Towards Agile Swarming in Real World: Onboard Relative Localization with
Fast Tracking of Active Blinking Markers | [
"cs.RO",
"cs.CV"
] | A novel onboard tracking approach enabling vision-based relative localization and communication using Active blinking Marker Tracking (AMT) is introduced in this article. Active blinking markers on multi-robot team members improve the robustness of relative localization for aerial vehicles in tightly coupled swarms dur... | {
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2502.01177 | Insights from Network Science can advance Deep Graph Learning | [
"cs.LG"
] | Deep graph learning and network science both analyze graphs but approach similar problems from different perspectives. Whereas network science focuses on models and measures that reveal the organizational principles of complex systems with explicit assumptions, deep graph learning focuses on flexible and generalizable ... | {
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2502.01179 | Joint Localization and Activation Editing for Low-Resource Fine-Tuning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, are commonly used to adapt LLMs. However, the effectiveness of standard PEFT methods is limited in low-resource scenarios with only a few hundred examples. Recent advances in interpretability research have inspired the emergence of activation editing techniq... | {
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2502.01180 | A Minimax Optimal Controller for Positive Systems | [
"math.OC",
"cs.SY",
"eess.SY"
] | We present an explicit solution to the discrete-time Bellman equation for minimax optimal control of positive systems under unconstrained disturbances. The primary contribution of our result relies on deducing a bound for the disturbance penalty, which characterizes the existence of a finite solution to the problem cla... | {
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2502.01181 | BVINet: Unlocking Blind Video Inpainting with Zero Annotations | [
"cs.CV"
] | Video inpainting aims to fill in corrupted regions of the video with plausible contents. Existing methods generally assume that the locations of corrupted regions are known, focusing primarily on the "how to inpaint". This reliance necessitates manual annotation of the corrupted regions using binary masks to indicate "... | {
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2502.01182 | A Single Model Ensemble Framework for Neural Machine Translation using
Pivot Translation | [
"cs.CL",
"cs.AI"
] | Despite the significant advances in neural machine translation, performance remains subpar for low-resource language pairs. Ensembling multiple systems is a widely adopted technique to enhance performance, often accomplished by combining probability distributions. However, the previous approaches face the challenge of ... | {
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2502.01183 | Enhancing Environmental Robustness in Few-shot Learning via Conditional
Representation Learning | [
"cs.CV"
] | Few-shot learning (FSL) has recently been extensively utilized to overcome the scarcity of training data in domain-specific visual recognition. In real-world scenarios, environmental factors such as complex backgrounds, varying lighting conditions, long-distance shooting, and moving targets often cause test images to e... | {
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2502.01184 | FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence
Molecular Representation Learning | [
"cs.LG",
"cs.AI",
"physics.chem-ph",
"q-bio.QM"
] | Molecular property prediction uses molecular structure to infer chemical properties. Chemically interpretable representations that capture meaningful intramolecular interactions enhance the usability and effectiveness of these predictions. However, existing methods often rely on atom-based or rule-based fragment tokeni... | {
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2502.01185 | Deep Active Speech Cancellation with Multi-Band Mamba Network | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS",
"eess.SP"
] | We present a novel deep learning network for Active Speech Cancellation (ASC), advancing beyond Active Noise Cancellation (ANC) methods by effectively canceling both noise and speech signals. The proposed Multi-Band Mamba architecture segments input audio into distinct frequency bands, enabling precise anti-signal gene... | {
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2502.01186 | A High-Accuracy SSIM-based Scoring System for Coin Die Link
Identification | [
"cs.CV"
] | The analyses of ancient coins, and especially the identification of those struck with the same die, provides invaluable information for archaeologists and historians. Nowadays, these die links are identified manually, which makes the process laborious, if not impossible when big treasures are discovered as the number o... | {
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2502.01187 | Skewed Memorization in Large Language Models: Quantification and
Decomposition | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Memorization in Large Language Models (LLMs) poses privacy and security risks, as models may unintentionally reproduce sensitive or copyrighted data. Existing analyses focus on average-case scenarios, often neglecting the highly skewed distribution of memorization. This paper examines memorization in LLM supervised fin... | {
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2502.01188 | FairUDT: Fairness-aware Uplift Decision Trees | [
"cs.LG",
"stat.ML"
] | Training data used for developing machine learning classifiers can exhibit biases against specific protected attributes. Such biases typically originate from historical discrimination or certain underlying patterns that disproportionately under-represent minority groups, such as those identified by their gender, religi... | {
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2502.01189 | Compressed Image Generation with Denoising Diffusion Codebook Models | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.IT",
"eess.SP",
"math.IT"
] | We present a novel generative approach based on Denoising Diffusion Models (DDMs), which produces high-quality image samples along with their losslessly compressed bit-stream representations. This is obtained by replacing the standard Gaussian noise sampling in the reverse diffusion with a selection of noise samples fr... | {
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2502.01190 | Dance recalibration for dance coherency with recurrent convolution block | [
"cs.LG",
"cs.AI"
] | With the recent advancements in generative AI such as GAN, Diffusion, and VAE, the use of generative AI for dance generation has seen significant progress and received considerable interest. In this study, We propose R-Lodge, an enhanced version of Lodge. R-Lodge incorporates Recurrent Sequential Representation Learnin... | {
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2502.01191 | Towards Robust and Reliable Concept Representations:
Reliability-Enhanced Concept Embedding Model | [
"cs.CV"
] | Concept Bottleneck Models (CBMs) aim to enhance interpretability by predicting human-understandable concepts as intermediates for decision-making. However, these models often face challenges in ensuring reliable concept representations, which can propagate to downstream tasks and undermine robustness, especially under ... | {
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2502.01194 | COVE: COntext and VEracity prediction for out-of-context images | [
"cs.CL"
] | Images taken out of their context are the most prevalent form of multimodal misinformation. Debunking them requires (1) providing the true context of the image and (2) checking the veracity of the image's caption. However, existing automated fact-checking methods fail to tackle both objectives explicitly. In this work,... | {
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2502.01197 | Multi-objective Evolution of Drone Morphology | [
"cs.RO"
] | The design of multicopter drones has remained almost the same since its inception. While conventional designs, such as the quadcopter, work well in many cases, they may not be optimal in specific environments or missions. This paper revisits rotary drone design by exploring which body morphologies are optimal for diffe... | {
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2502.01199 | Nearly Lossless Adaptive Bit Switching | [
"cs.CV",
"cs.AI"
] | Model quantization is widely applied for compressing and accelerating deep neural networks (DNNs). However, conventional Quantization-Aware Training (QAT) focuses on training DNNs with uniform bit-width. The bit-width settings vary across different hardware and transmission demands, which induces considerable training ... | {
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2502.01201 | One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection | [
"cs.CV"
] | Traditional Anomaly Detection (AD) methods have predominantly relied on unsupervised learning from extensive normal data. Recent AD methods have evolved with the advent of large pre-trained vision-language models, enhancing few-shot anomaly detection capabilities. However, these latest AD methods still exhibit limitati... | {
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2502.01203 | Theoretical Analysis of KL-regularized RLHF with Multiple Reference
Models | [
"cs.LG",
"stat.ML"
] | Recent methods for aligning large language models (LLMs) with human feedback predominantly rely on a single reference model, which limits diversity, model overfitting, and underutilizes the wide range of available pre-trained models. Incorporating multiple reference models has the potential to address these limitations... | {
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2502.01204 | Land Surface Temperature Super-Resolution with a Scale-Invariance-Free
Neural Approach: Application to MODIS | [
"cs.LG",
"cs.CV"
] | Due to the trade-off between the temporal and spatial resolution of thermal spaceborne sensors, super-resolution methods have been developed to provide fine-scale Land SurfaceTemperature (LST) maps. Most of them are trained at low resolution but applied at fine resolution, and so they require a scale-invariance hypothe... | {
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2502.01205 | OCR Error Post-Correction with LLMs in Historical Documents: No Free
Lunches | [
"cs.CL"
] | Optical Character Recognition (OCR) systems often introduce errors when transcribing historical documents, leaving room for post-correction to improve text quality. This study evaluates the use of open-weight LLMs for OCR error correction in historical English and Finnish datasets. We explore various strategies, includ... | {
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2502.01207 | Solgenia -- A Test Vessel Toward Energy-Efficient Autonomous Water Taxi
Applications | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Autonomous surface vessels are a promising building block of the future's transport sector and are investigated by research groups worldwide. This paper presents a comprehensive and systematic overview of the autonomous research vessel Solgenia including the latest investigations and recently presented methods that con... | {
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2502.01208 | Almost Surely Safe Alignment of Large Language Models at Inference-Time | [
"cs.LG",
"cs.CL"
] | Even highly capable large language models (LLMs) can produce biased or unsafe responses, and alignment techniques, such as RLHF, aimed at mitigating this issue, are expensive and prone to overfitting as they retrain the LLM. This paper introduces a novel inference-time alignment approach that ensures LLMs generate safe... | {
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2502.01210 | Modelling change in neural dynamics during phonetic accommodation | [
"cs.CL"
] | Short-term phonetic accommodation is a fundamental driver behind accent change, but how does real-time input from another speaker's voice shape the speech planning representations of an interlocutor? We advance a computational model of change in phonetic representations during phonetic accommodation, grounded in dynami... | {
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2502.01211 | Privilege Scores | [
"cs.LG",
"stat.ML"
] | Bias-transforming methods of fairness-aware machine learning aim to correct a non-neutral status quo with respect to a protected attribute (PA). Current methods, however, lack an explicit formulation of what drives non-neutrality. We introduce privilege scores (PS) to measure PA-related privilege by comparing the model... | {
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} |
2502.01216 | Exploring Few-Shot Defect Segmentation in General Industrial Scenarios
with Metric Learning and Vision Foundation Models | [
"cs.CV"
] | Industrial defect segmentation is critical for manufacturing quality control. Due to the scarcity of training defect samples, few-shot semantic segmentation (FSS) holds significant value in this field. However, existing studies mostly apply FSS to tackle defects on simple textures, without considering more diverse scen... | {
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2502.01218 | Provable Ordering and Continuity in Vision-Language Pretraining for
Generalizable Embodied Agents | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | Pre-training vision-language representations on human action videos has emerged as a promising approach to reduce reliance on large-scale expert demonstrations for training embodied agents. However, prior methods often employ time contrastive learning based on goal-reaching heuristics, progressively aligning language i... | {
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} |
2502.01220 | Language Models Struggle to Achieve a Consistent Temporal Representation
of Facts | [
"cs.CL",
"cs.LG"
] | Language Models (LMs) have shown substantial improvements in handling factual knowledge, yet their capability to consistently represent temporal facts, which are valid only within specific timeframes, remains underexplored. To investigate this, we introduce TimeStress, a novel dataset comprising 521K statements on 2003... | {
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2502.01225 | The dark deep side of DeepSeek: Fine-tuning attacks against the safety
alignment of CoT-enabled models | [
"cs.CR",
"cs.AI"
] | Large language models are typically trained on vast amounts of data during the pre-training phase, which may include some potentially harmful information. Fine-tuning attacks can exploit this by prompting the model to reveal such behaviours, leading to the generation of harmful content. In this paper, we focus on inves... | {
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2502.01226 | Efficient Prior Selection in Gaussian Process Bandits with Thompson
Sampling | [
"cs.LG",
"stat.ML"
] | Gaussian process (GP) bandits provide a powerful framework for solving blackbox optimization of unknown functions. The characteristics of the unknown function depends heavily on the assumed GP prior. Most work in the literature assume that this prior is known but in practice this seldom holds. Instead, practitioners of... | {
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2502.01228 | Soft Robot Localization Using Distributed Miniaturized Time-of-Flight
Sensors | [
"cs.RO"
] | Thanks to their compliance and adaptability, soft robots can be deployed to perform tasks in constrained or complex environments. In these scenarios, spatial awareness of the surroundings and the ability to localize the robot within the environment represent key aspects. While state-of-the-art localization techniques a... | {
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2502.01229 | How Good are Learned Cost Models, Really? Insights from Query
Optimization Tasks | [
"cs.DB"
] | Traditionally, query optimizers rely on cost models to choose the best execution plan from several candidates, making precise cost estimates critical for efficient query execution. In recent years, cost models based on machine learning have been proposed to overcome the weaknesses of traditional cost models. While thes... | {
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2502.01231 | Societal Attitudes Toward Service Robots: Adore, Abhor, Ignore, or
Unsure? | [
"cs.RO"
] | Societal or population-level attitudes are aggregated patterns of different individual attitudes, representing collective general predispositions. As service robots become ubiquitous, understanding attitudes towards them at the population (vs. individual) level enables firms to expand robot services to a broad (vs. nic... | {
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2502.01232 | Efficient rule induction by ignoring pointless rules | [
"cs.AI"
] | The goal of inductive logic programming (ILP) is to find a set of logical rules that generalises training examples and background knowledge. We introduce an ILP approach that identifies pointless rules. A rule is pointless if it contains a redundant literal or cannot discriminate against negative examples. We show that... | {
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2502.01235 | One-step full gradient suffices for low-rank fine-tuning, provably and
efficiently | [
"stat.ML",
"cs.AI",
"cs.LG"
] | This paper studies how to improve the performance of Low-Rank Adaption (LoRA) as guided by our theoretical analysis. Our first set of theoretical results show that for random initialization and linear models, \textit{i)} LoRA will align to the certain singular subspace of one-step gradient of full fine-tuning; \textit{... | {
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2502.01236 | Eliciting Language Model Behaviors with Investigator Agents | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Language models exhibit complex, diverse behaviors when prompted with free-form text, making it difficult to characterize the space of possible outputs. We study the problem of behavior elicitation, where the goal is to search for prompts that induce specific target behaviors (e.g., hallucinations or harmful responses)... | {
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2502.01237 | The Differences Between Direct Alignment Algorithms are a Blur | [
"cs.LG"
] | Direct Alignment Algorithms (DAAs) simplify language model alignment by replacing reinforcement learning (RL) and reward modeling (RM) in Reinforcement Learning from Human Feedback (RLHF) with direct policy optimization. DAAs can be classified by their ranking losses (pairwise vs. pointwise), by the rewards used in tho... | {
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2502.01242 | Neural Cellular Automata for Decentralized Sensing using a Soft
Inductive Sensor Array for Distributed Manipulator Systems | [
"cs.RO",
"cs.LG"
] | In Distributed Manipulator Systems (DMS), decentralization is a highly desirable property as it promotes robustness and facilitates scalability by distributing computational burden and eliminating singular points of failure. However, current DMS typically utilize a centralized approach to sensing, such as single-camera... | {
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2502.01243 | OphthBench: A Comprehensive Benchmark for Evaluating Large Language
Models in Chinese Ophthalmology | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have shown significant promise across various medical applications, with ophthalmology being a notable area of focus. Many ophthalmic tasks have shown substantial improvement through the integration of LLMs. However, before these models can be widely adopted in clinical practice, evaluating... | {
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2502.01247 | Learnable polynomial, trigonometric, and tropical activations | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV",
"math.AG"
] | This paper investigates scalable neural networks with learnable activation functions based on orthogonal function bases and tropical polynomials, targeting ImageNet-1K classification and next token prediction on OpenWebText. Traditional activations, such as ReLU, are static. In contrast, learnable activations enable th... | {
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2502.01248 | Computational modelling of cancer nanomedicine: Integrating hyperthermia
treatment into a multiphase porous-media tumour model | [
"cs.CE"
] | Heat-based cancer treatment, so-called hyperthermia, can be used to destroy tumour cells directly or to make them more susceptible to chemotherapy or radiation therapy. To apply heat locally, iron oxide nanoparticles are injected into the bloodstream and accumulate at the tumour site, where they generate heat when expo... | {
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2502.01250 | Beyond Win Rates: A Clustering-Based Approach to Character Balance
Analysis in Team-Based Games | [
"cs.LG"
] | Character diversity in competitive games, while enriching gameplay, often introduces balance challenges that can negatively impact player experience and strategic depth. Traditional balance assessments rely on aggregate metrics like win rates and pick rates, which offer limited insight into the intricate dynamics of te... | {
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2502.01253 | Explainability-Driven Quality Assessment for Rule-Based Systems | [
"cs.AI",
"cs.LO"
] | This paper introduces an explanation framework designed to enhance the quality of rules in knowledge-based reasoning systems based on dataset-driven insights. The traditional method for rule induction from data typically requires labor-intensive labeling and data-driven learning. This framework provides an alternative ... | {
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2502.01256 | Soft is Safe: Human-Robot Interaction for Soft Robots | [
"cs.RO"
] | With the presence of robots increasing in the society, the need for interacting with robots is becoming necessary. The field of Human-Robot Interaction (HRI) has emerged important since more repetitive and tiresome jobs are being done by robots. In the recent times, the field of soft robotics has seen a boom in the fie... | {
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2502.01262 | FSPGD: Rethinking Black-box Attacks on Semantic Segmentation | [
"cs.CV"
] | Transferability, the ability of adversarial examples crafted for one model to deceive other models, is crucial for black-box attacks. Despite advancements in attack methods for semantic segmentation, transferability remains limited, reducing their effectiveness in real-world applications. To address this, we introduce ... | {
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2502.01264 | Generalized Lanczos method for systematic optimization of neural-network
quantum states | [
"cond-mat.str-el",
"cs.LG",
"physics.comp-ph"
] | Recently, artificial intelligence for science has made significant inroads into various fields of natural science research. In the field of quantum many-body computation, researchers have developed numerous ground state solvers based on neural-network quantum states (NQSs), achieving ground state energies with accuracy... | {
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2502.01265 | On Exact Learning of $d$-Monotone Functions | [
"cs.LG",
"cs.DS"
] | In this paper, we study the learnability of the Boolean class of $d$-monotone functions $f:{\cal X}\to\{0,1\}$ from membership and equivalence queries, where $({\cal X},\le)$ is a finite lattice. We show that the class of $d$-monotone functions that are represented in the form $f=F(g_1,g_2,\ldots,g_d)$, where $F$ is an... | {
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2502.01267 | Counterfactual Situation Testing: From Single to Multidimensional
Discrimination | [
"cs.LG"
] | We present counterfactual situation testing (CST), a causal data mining framework for detecting individual discrimination in a dataset of classifier decisions. CST answers the question "what would have been the model outcome had the individual, or complainant, been of a different protected status?" It extends the legal... | {
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2502.01268 | Resilient UAV Trajectory Planning via Few-Shot Meta-Offline
Reinforcement Learning | [
"cs.RO",
"cs.AI"
] | Reinforcement learning (RL) has been a promising essence in future 5G-beyond and 6G systems. Its main advantage lies in its robust model-free decision-making in complex and large-dimension wireless environments. However, most existing RL frameworks rely on online interaction with the environment, which might not be fea... | {
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2502.01269 | Exploratory Utility Maximization Problem with Tsallis Entropy | [
"cs.LG",
"q-fin.MF"
] | We study expected utility maximization problem with constant relative risk aversion utility function in a complete market under the reinforcement learning framework. To induce exploration, we introduce the Tsallis entropy regularizer, which generalizes the commonly used Shannon entropy. Unlike the classical Merton's pr... | {
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2502.01270 | Main Predicate and Their Arguments as Explanation Signals For Intent
Classification | [
"cs.CL"
] | Intent classification is crucial for conversational agents (chatbots), and deep learning models perform well in this area. However, little research has been done on the explainability of intent classification due to the absence of suitable benchmark data. Human annotation of explanation signals in text samples is time-... | {
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2502.01272 | Boosting Graph Robustness Against Backdoor Attacks: An Over-Similarity
Perspective | [
"cs.LG"
] | Graph Neural Networks (GNNs) have achieved notable success in tasks such as social and transportation networks. However, recent studies have highlighted the vulnerability of GNNs to backdoor attacks, raising significant concerns about their reliability in real-world applications. Despite initial efforts to defend again... | {
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2502.01273 | Analysis of Student-LLM Interaction in a Software Engineering Project | [
"cs.SE",
"cs.AI"
] | Large Language Models (LLMs) are becoming increasingly competent across various domains, educators are showing a growing interest in integrating these LLMs into the learning process. Especially in software engineering, LLMs have demonstrated qualitatively better capabilities in code summarization, code generation, and ... | {
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2502.01276 | HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Hyperparameter optimization (HPO) is a crucial step in achieving strong predictive performance. However, the impact of individual hyperparameters on model generalization is highly context-dependent, prohibiting a one-size-fits-all solution and requiring opaque automated machine learning (AutoML) systems to find optimal... | {
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2502.01278 | DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for
Mobile Users | [
"eess.SP",
"cs.LG"
] | With the emergence of AI technologies in next-generation communication systems, machine learning plays a pivotal role due to its ability to address high-dimensional, non-stationary optimization problems within dynamic environments while maintaining computational efficiency. One such application is directional beamformi... | {
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2502.01280 | Trajectory Map-Matching in Urban Road Networks Based on RSS Measurements | [
"eess.SY",
"cs.SY"
] | This paper proposes an RSS-based approach to reconstruct vehicle trajectories within a road network, enforcing signal propagation rules and vehicle mobility constraints to mitigate the impact of RSS noise and sparsity. The key challenge lies in leveraging latent spatiotemporal correlations within RSS data while navigat... | {
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2502.01281 | Label Correction for Road Segmentation Using Road-side Cameras | [
"cs.CV"
] | Reliable road segmentation in all weather conditions is critical for intelligent transportation applications, autonomous vehicles and advanced driver's assistance systems. For robust performance, all weather conditions should be included in the training data of deep learning-based perception models. However, collecting... | {
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2502.01282 | Rational Gaussian wavelets and corresponding model driven neural
networks | [
"stat.ML",
"cs.AI",
"cs.LG"
] | In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate sig... | {
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2502.01286 | Template Matching in Images using Segmented Normalized Cross-Correlation | [
"cs.CV"
] | In this paper, a new variant of an algorithm for normalized cross-correlation (NCC) is proposed in the context of template matching in images. The proposed algorithm is based on the precomputation of a template image approximation, enabling more efficient calculation of approximate NCC with the source image than using ... | {
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2502.01289 | A Framework for Double-Blind Federated Adaptation of Foundation Models | [
"cs.LG",
"cs.CR",
"cs.CV",
"cs.DC"
] | The availability of foundational models (FMs) pre-trained on large-scale data has advanced the state-of-the-art in many computer vision tasks. While FMs have demonstrated good zero-shot performance on many image classification tasks, there is often scope for performance improvement by adapting the FM to the downstream ... | {
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2502.01295 | Common Foundations for SHACL, ShEx, and PG-Schema | [
"cs.DB",
"cs.AI"
] | Graphs have emerged as an important foundation for a variety of applications, including capturing and reasoning over factual knowledge, semantic data integration, social networks, and providing factual knowledge for machine learning algorithms. To formalise certain properties of the data and to ensure data quality, the... | {
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} |
2502.01296 | Molecular Odor Prediction with Harmonic Modulated Feature Mapping and
Chemically-Informed Loss | [
"cs.LG",
"q-bio.QM"
] | Molecular odor prediction has great potential across diverse fields such as chemistry, pharmaceuticals, and environmental science, enabling the rapid design of new materials and enhancing environmental monitoring. However, current methods face two main challenges: First, existing models struggle with non-smooth objecti... | {
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} |
2502.01297 | XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization
for XR Applications | [
"cs.CV"
] | This paper presents a novel approach to Visual Inertial Odometry (VIO), focusing on the initialization and feature matching modules. Existing methods for initialization often suffer from either poor stability in visual Structure from Motion (SfM) or fragility in solving a huge number of parameters simultaneously. To ad... | {
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} |
2502.01298 | Augmented Knowledge Graph Querying leveraging LLMs | [
"cs.IR"
] | Adopting Knowledge Graphs (KGs) as a structured, semantic-oriented, data representation model has significantly improved data integration, reasoning, and querying capabilities across different domains. This is especially true in modern scenarios such as Industry 5.0, in which the integration of data produced by humans,... | {
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} |
2502.01299 | Probabilistic adaptation of language comprehension for individual
speakers: Evidence from neural oscillations | [
"q-bio.NC",
"cs.CL"
] | Listeners adapt language comprehension based on their mental representations of speakers, but how these representations are dynamically updated remains unclear. We investigated whether listeners probabilistically adapt their comprehension based on the likelihood of speakers producing stereotype-incongruent utterances. ... | {
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} |
2502.01303 | Partial Channel Network: Compute Fewer, Perform Better | [
"cs.CV",
"cs.AI"
] | Designing a module or mechanism that enables a network to maintain low parameters and FLOPs without sacrificing accuracy and throughput remains a challenge. To address this challenge and exploit the redundancy within feature map channels, we propose a new solution: partial channel mechanism (PCM). Specifically, through... | {
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} |
2502.01304 | Towards Autonomous Wood-Log Grasping with a Forestry Crane: Simulator
and Benchmarking | [
"cs.RO"
] | Forestry machines operated in forest production environments face challenges when performing manipulation tasks, especially regarding the complicated dynamics of underactuated crane systems and the heavy weight of logs to be grasped. This study investigates the feasibility of using reinforcement learning for forestry c... | {
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} |
2502.01307 | Improving the Effectiveness of Potential-Based Reward Shaping in
Reinforcement Learning | [
"cs.LG"
] | Potential-based reward shaping is commonly used to incorporate prior knowledge of how to solve the task into reinforcement learning because it can formally guarantee policy invariance. As such, the optimal policy and the ordering of policies by their returns are not altered by potential-based reward shaping. In this wo... | {
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} |
2502.01309 | Heterogeneous Image GNN: Graph-Conditioned Diffusion for Image Synthesis | [
"cs.CV"
] | We introduce a novel method for conditioning diffusion-based image synthesis models with heterogeneous graph data. Existing approaches typically incorporate conditioning variables directly into model architectures, either through cross-attention layers that attend to text latents or image concatenation that spatially r... | {
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} |
2502.01310 | A Statistical Learning Perspective on Semi-dual Adversarial Neural
Optimal Transport Solvers | [
"cs.LG",
"cs.AI"
] | Neural network based Optimal Transport (OT) is a recent and fruitful direction in the generative modeling community. It finds its applications in various fields such as domain translation, image super-resolution, computational biology and others. Among the existing approaches to OT, of considerable interest are adversa... | {
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} |
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