id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.14178 | NeRF-3DTalker: Neural Radiance Field with 3D Prior Aided Audio
Disentanglement for Talking Head Synthesis | [
"cs.GR",
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | Talking head synthesis is to synthesize a lip-synchronized talking head video using audio. Recently, the capability of NeRF to enhance the realism and texture details of synthesized talking heads has attracted the attention of researchers. However, most current NeRF methods based on audio are exclusively concerned with... | {
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2502.14180 | On the logical skills of large language models: evaluations using
arbitrarily complex first-order logic problems | [
"cs.LG",
"cs.CL"
] | We present a method of generating first-order logic statements whose complexity can be controlled along multiple dimensions. We use this method to automatically create several datasets consisting of questions asking for the truth or falsity of first-order logic statements in Zermelo-Fraenkel set theory. While the resol... | {
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2502.14182 | Multi-Faceted Studies on Data Poisoning can Advance LLM Development | [
"cs.CR",
"cs.LG"
] | The lifecycle of large language models (LLMs) is far more complex than that of traditional machine learning models, involving multiple training stages, diverse data sources, and varied inference methods. While prior research on data poisoning attacks has primarily focused on the safety vulnerabilities of LLMs, these at... | {
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2502.14183 | Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model
with Modified Error Rate | [
"cs.LG",
"cs.AI"
] | Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avert the dangers of dysglycemia (hyperglycemia or hypoglycemia). Despite the advent of sophisticated technologies such as automated insulin delivery (AID) systems, achieving optimal glycemic contro... | {
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2502.14184 | Bayesian SegNet for Semantic Segmentation with Improved Interpretation
of Microstructural Evolution During Irradiation of Materials | [
"cs.CV",
"cs.LG"
] | Understanding the relationship between the evolution of microstructures of irradiated LiAlO2 pellets and tritium diffusion, retention and release could improve predictions of tritium-producing burnable absorber rod performance. Given expert-labeled segmented images of irradiated and unirradiated pellets, we trained Dee... | {
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2502.14185 | REFLEX Dataset: A Multimodal Dataset of Human Reactions to Robot
Failures and Explanations | [
"cs.RO"
] | This work presents REFLEX: Robotic Explanations to FaiLures and Human EXpressions, a comprehensive multimodal dataset capturing human reactions to robot failures and subsequent explanations in collaborative settings. It aims to facilitate research into human-robot interaction dynamics, addressing the need to study reac... | {
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2502.14187 | Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs.
Direct Preference Optimization | [
"cs.LG",
"cs.CL"
] | We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for large language models (LLMs) in federated learning (FL) settings, comparing it against Direct Preference Optimization (DPO). Using Alpaca-7B as the base model, we fine-tune on a realistic dataset under both methods and evaluate performance usin... | {
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2502.14189 | QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare
Text Multi-Label Classification | [
"cs.CL"
] | The escalating volume of collected healthcare textual data presents a unique challenge for automated Multi-Label Text Classification (MLTC), which is primarily due to the scarcity of annotated texts for training and their nuanced nature. Traditional machine learning models often fail to fully capture the array of expre... | {
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2502.14190 | Stereo Image Coding for Machines with Joint Visual Feature Compression | [
"cs.CV",
"eess.IV"
] | 2D image coding for machines (ICM) has achieved great success in coding efficiency, while less effort has been devoted to stereo image fields. To promote the efficiency of stereo image compression (SIC) and intelligent analysis, the stereo image coding for machines (SICM) is formulated and explored in this paper. More ... | {
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2502.14191 | Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision
Language Models | [
"cs.CV",
"cs.AI"
] | Reward models play an essential role in training vision-language models (VLMs) by assessing output quality to enable aligning with human preferences. Despite their importance, the research community lacks comprehensive open benchmarks for evaluating multimodal reward models in VLMs. To address this gap, we introduce Mu... | {
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2502.14192 | NLP-AKG: Few-Shot Construction of NLP Academic Knowledge Graph Based on
LLM | [
"cs.CL",
"cs.DL"
] | Large language models (LLMs) have been widely applied in question answering over scientific research papers. To enhance the professionalism and accuracy of responses, many studies employ external knowledge augmentation. However, existing structures of external knowledge in scientific literature often focus solely on ei... | {
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2502.14195 | Bridging Text and Vision: A Multi-View Text-Vision Registration Approach
for Cross-Modal Place Recognition | [
"cs.CV"
] | Mobile robots necessitate advanced natural language understanding capabilities to accurately identify locations and perform tasks such as package delivery. However, traditional visual place recognition (VPR) methods rely solely on single-view visual information and cannot interpret human language descriptions. To overc... | {
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2502.14197 | Adaptive Sparsified Graph Learning Framework for Vessel Behavior
Anomalies | [
"cs.LG",
"cs.AI"
] | Graph neural networks have emerged as a powerful tool for learning spatiotemporal interactions. However, conventional approaches often rely on predefined graphs, which may obscure the precise relationships being modeled. Additionally, existing methods typically define nodes based on fixed spatial locations, a strategy ... | {
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2502.14198 | Antenna Position and Beamforming Optimization for Movable Antenna
Enabled ISAC: Optimal Solutions and Efficient Algorithms | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper, we propose an integrated sensing and communication (ISAC) system enabled by movable antennas (MAs), which can dynamically adjust antenna positions to enhance both sensing and communication performance for future wireless networks. To characterize the benefits of MA-enabled ISAC systems, we first derive t... | {
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2502.14200 | Causal Mean Field Multi-Agent Reinforcement Learning | [
"cs.AI",
"cs.MA"
] | Scalability remains a challenge in multi-agent reinforcement learning and is currently under active research. A framework named mean-field reinforcement learning (MFRL) could alleviate the scalability problem by employing the Mean Field Theory to turn a many-agent problem into a two-agent problem. However, this framewo... | {
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2502.14202 | Do LLMs Consider Security? An Empirical Study on Responses to
Programming Questions | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.LG"
] | The widespread adoption of conversational LLMs for software development has raised new security concerns regarding the safety of LLM-generated content. Our motivational study outlines ChatGPT's potential in volunteering context-specific information to the developers, promoting safe coding practices. Motivated by this f... | {
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2502.14204 | On-the-fly Preference Alignment via Principle-Guided Decoding | [
"cs.CL",
"cs.AI"
] | With the rapidly expanding landscape of large language models, aligning model generations with human values and preferences is becoming increasingly important. Popular alignment methods, such as Reinforcement Learning from Human Feedback, have shown significant success in guiding models with greater control. However, t... | {
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2502.14205 | Accurate Forgetting for Heterogeneous Federated Continual Learning | [
"cs.LG",
"cs.AI"
] | Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging FL and continual learning (CL) gives rise to a challenging practical problem: federated continual learning (FCL). Existing research in FCL pr... | {
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2502.14208 | A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From
Deterministic to Stochastic Iterative Algorithms | [
"cs.LG",
"math.OC",
"stat.ML"
] | We study the problem of solving fixed-point equations for seminorm-contractive operators and establish foundational results on the non-asymptotic behavior of iterative algorithms in both deterministic and stochastic settings. Specifically, in the deterministic setting, we prove a fixed-point theorem for seminorm-contra... | {
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2502.14209 | Spatial and Frequency Domain Adaptive Fusion Network for Image
Deblurring | [
"cs.CV"
] | Image deblurring aims to reconstruct a latent sharp image from its corresponding blurred one. Although existing methods have achieved good performance, most of them operate exclusively in either the spatial domain or the frequency domain, rarely exploring solutions that fuse both domains. In this paper, we propose a sp... | {
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2502.14210 | Sample Complexity of Linear Quadratic Regulator Without Initial
Stability | [
"math.OC",
"cs.LG",
"cs.SY",
"eess.SY"
] | Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem with unknown parameters. Unlike prior methods, our algorithm avoids reliance on two-point gradient estimates while maintaining the same order of sample complexity. Furthermore, it eliminates the restr... | {
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2502.14211 | Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language
Models via Dual-Stage Prompts Optimization | [
"cs.CL"
] | Large language models (LLMs) face significant challenges when balancing multiple high-level objectives, such as generating coherent, relevant, and high-quality responses while maintaining efficient task adaptation across diverse tasks. To address these challenges, we introduce Transfer-Prompting, a novel two-stage fram... | {
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2502.14212 | Less is More: On the Importance of Data Quality for Unit Test Generation | [
"cs.SE",
"cs.IR"
] | Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-intensive. Recent studies have proposed deep learning (DL) techniques or large language models (LLMs) to automate unit test generation. These... | {
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2502.14214 | Asymmetric Co-Training for Source-Free Few-Shot Domain Adaptation | [
"cs.LG",
"cs.CV"
] | Source-free unsupervised domain adaptation (SFUDA) has gained significant attention as an alternative to traditional unsupervised domain adaptation (UDA), which relies on the constant availability of labeled source data. However, SFUDA approaches come with inherent limitations that are frequently overlooked. These chal... | {
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2502.14215 | Towards Secure Program Partitioning for Smart Contracts with LLM's
In-Context Learning | [
"cs.SE",
"cs.AI"
] | Smart contracts are highly susceptible to manipulation attacks due to the leakage of sensitive information. Addressing manipulation vulnerabilities is particularly challenging because they stem from inherent data confidentiality issues rather than straightforward implementation bugs. To tackle this by preventing sensit... | {
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2502.14218 | Rethinking Spiking Neural Networks from an Ensemble Learning Perspective | [
"cs.LG",
"cs.AI"
] | Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that share architectures and weights, and highlight a crucial issue that affects their performance: excessive differences in initial states (neurona... | {
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2502.14219 | Investigating the Impact of LLM Personality on Cognitive Bias
Manifestation in Automated Decision-Making Tasks | [
"cs.AI"
] | Large Language Models (LLMs) are increasingly used in decision-making, yet their susceptibility to cognitive biases remains a pressing challenge. This study explores how personality traits influence these biases and evaluates the effectiveness of mitigation strategies across various model architectures. Our findings id... | {
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2502.14221 | H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical
Imaging | [
"cs.CV"
] | 3D landmark detection is a critical task in medical image analysis, and accurately detecting anatomical landmarks is essential for subsequent medical imaging tasks. However, mainstream deep learning methods in this field struggle to simultaneously capture fine-grained local features and model global spatial relationshi... | {
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2502.14222 | Enhancing Pavement Sensor Data Acquisition for AI-Driven Transportation
Research | [
"cs.DB",
"cs.AI",
"eess.SP"
] | Effective strategies for sensor data management are essential for advancing transportation research, especially in the current data-driven era, due to the advent of novel applications in artificial intelligence. This paper presents comprehensive guidelines for managing transportation sensor data, encompassing both arch... | {
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2502.14226 | Designing Parameter and Compute Efficient Diffusion Transformers using
Distillation | [
"cs.CV",
"eess.IV"
] | Diffusion Transformers (DiTs) with billions of model parameters form the backbone of popular image and video generation models like DALL.E, Stable-Diffusion and SORA. Though these models are necessary in many low-latency applications like Augmented/Virtual Reality, they cannot be deployed on resource-constrained Edge d... | {
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2502.14227 | SleepGMUformer: A gated multimodal temporal neural network for sleep
staging | [
"cs.LG",
"cs.AI"
] | Sleep staging is a key method for assessing sleep quality and diagnosing sleep disorders. However, current deep learning methods face challenges: 1) postfusion techniques ignore the varying contributions of different modalities; 2) unprocessed sleep data can interfere with frequency-domain information. To tackle these ... | {
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2502.14231 | Real-Time Sampling-based Online Planning for Drone Interception | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY"
] | This paper studies high-speed online planning in dynamic environments. The problem requires finding time-optimal trajectories that conform to system dynamics, meeting computational constraints for real-time adaptation, and accounting for uncertainty from environmental changes. To address these challenges, we propose a ... | {
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2502.14234 | OBELiX: A Curated Dataset of Crystal Structures and Experimentally
Measured Ionic Conductivities for Lithium Solid-State Electrolytes | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently hindered by their lower effective ionic conductivity, a quantity that governs charge and disch... | {
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2502.14235 | OG-Gaussian: Occupancy Based Street Gaussians for Autonomous Driving | [
"cs.CV",
"cs.AI"
] | Accurate and realistic 3D scene reconstruction enables the lifelike creation of autonomous driving simulation environments. With advancements in 3D Gaussian Splatting (3DGS), previous studies have applied it to reconstruct complex dynamic driving scenes. These methods typically require expensive LiDAR sensors and pre-a... | {
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2502.14238 | No Minima, No Collisions: Combining Modulation and Control Barrier
Function Strategies for Feasible Dynamical Collision Avoidance | [
"cs.RO",
"cs.SY",
"eess.SY"
] | As prominent real-time safety-critical reactive control techniques, Control Barrier Function Quadratic Programs (CBF-QPs) work for control affine systems in general but result in local minima in the generated trajectories and consequently cannot ensure convergence to the goals. Contrarily, Modulation of Dynamical Syste... | {
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2502.14242 | On the Contraction Analysis of Nonlinear System with Multiple
Equilibrium Points | [
"eess.SY",
"cs.SY"
] | In this work, we leverage the 2-contraction theory, which extends the capabilities of classical contraction theory, to develop a global stability framework. Coupled with powerful geometric tools such as the Poincare index theory, the 2-contraction theory enables us to analyze the stability of planar nonlinear systems w... | {
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2502.14245 | Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop
Question Answering | [
"cs.CL"
] | In this paper, we identify a critical problem, "lost-in-retrieval", in retrieval-augmented multi-hop question answering (QA): the key entities are missed in LLMs' sub-question decomposition. "Lost-in-retrieval" significantly degrades the retrieval performance, which disrupts the reasoning chain and leads to the incorre... | {
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2502.14247 | Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and
Texture Generation | [
"cs.GR",
"cs.AI",
"cs.CV"
] | This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts, including single images, multi-view images, and text descriptions. The framework consists of 3D shape generation and texture generation. (1). The 3D shape generation pipeline employs a Variation... | {
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2502.14251 | Bayesian Parameter Inference and Uncertainty Quantification for a
Computational Pulmonary Hemodynamics Model Using Gaussian Processes | [
"stat.AP",
"cs.CE",
"physics.bio-ph"
] | Patient-specific modeling is a valuable tool in cardiovascular disease research, offering insights beyond what current clinical equipment can measure. Given the limitations of available clinical data, models that incorporate uncertainty can provide clinicians with better guidance for tailored treatments. However, such ... | {
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2502.14252 | Towards efficient quantum algorithms for diffusion probability models | [
"quant-ph",
"cs.LG"
] | A diffusion probabilistic model (DPM) is a generative model renowned for its ability to produce high-quality outputs in tasks such as image and audio generation. However, training DPMs on large, high-dimensional datasets such as high-resolution images or audio incurs significant computational, energy, and hardware cost... | {
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2502.14254 | Mem2Ego: Empowering Vision-Language Models with Global-to-Ego Memory for
Long-Horizon Embodied Navigation | [
"cs.RO",
"cs.AI"
] | Recent advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have made them powerful tools in embodied navigation, enabling agents to leverage commonsense and spatial reasoning for efficient exploration in unfamiliar environments. Existing LLM-based approaches convert global memory, such as sem... | {
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2502.14255 | Effects of Prompt Length on Domain-specific Tasks for Large Language
Models | [
"cs.CL",
"cs.AI",
"cs.ET",
"cs.LG"
] | In recent years, Large Language Models have garnered significant attention for their strong performance in various natural language tasks, such as machine translation and question answering. These models demonstrate an impressive ability to generalize across diverse tasks. However, their effectiveness in tackling domai... | {
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2502.14258 | Does Time Have Its Place? Temporal Heads: Where Language Models Recall
Time-specific Information | [
"cs.CL",
"cs.AI"
] | While the ability of language models to elicit facts has been widely investigated, how they handle temporally changing facts remains underexplored. We discover Temporal Heads, specific attention heads primarily responsible for processing temporal knowledge through circuit analysis. We confirm that these heads are prese... | {
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2502.14259 | LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic
Health Records | [
"cs.LG"
] | Lab tests are fundamental for diagnosing diseases and monitoring patient conditions. However, frequent testing can be burdensome for patients, and test results may not always be immediately available. To address these challenges, we propose LabTOP, a unified model that predicts lab test outcomes by leveraging a languag... | {
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2502.14260 | EyeBench: A Call for More Rigorous Evaluation of Retinal Image
Enhancement | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Over the past decade, generative models have achieved significant success in enhancement fundus images.However, the evaluation of these models still presents a considerable challenge. A comprehensive evaluation benchmark for fundus image enhancement is indispensable for three main reasons: 1) The existing denoising met... | {
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2502.14264 | SPRIG: Stackelberg Perception-Reinforcement Learning with Internal Game
Dynamics | [
"cs.AI"
] | Deep reinforcement learning agents often face challenges to effectively coordinate perception and decision-making components, particularly in environments with high-dimensional sensory inputs where feature relevance varies. This work introduces SPRIG (Stackelberg Perception-Reinforcement learning with Internal Game dyn... | {
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2502.14267 | Money Recognition for the Visually Impaired: A Case Study on Sri Lankan
Banknotes | [
"cs.CV"
] | Currency note recognition is a critical accessibility need for blind individuals, as identifying banknotes accurately can impact their independence and security in financial transactions. Several traditional and technological initiatives have been taken to date. Nevertheless, these approaches are less user-friendly and... | {
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2502.14268 | MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard
Correctness Labels | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) require robust confidence estimation, particularly in critical domains like healthcare and law where unreliable outputs can lead to significant consequences. Despite much recent work in confidence estimation, current evaluation frameworks rely on correctness functions -- various heuristics ... | {
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2502.14270 | Predicting Fetal Birthweight from High Dimensional Data using Advanced
Machine Learning | [
"cs.LG"
] | Birth weight serves as a fundamental indicator of neonatal health, closely linked to both early medical interventions and long-term developmental risks. Traditional predictive models, often constrained by limited feature selection and incomplete datasets, struggle to achieve overlooking complex maternal and fetal inter... | {
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2502.14271 | PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant | [
"cs.CL"
] | In the paper, we introduce a paper reading assistant, PaperHelper, a potent tool designed to enhance the capabilities of researchers in efficiently browsing and understanding scientific literature. Utilizing the Retrieval-Augmented Generation (RAG) framework, PaperHelper effectively minimizes hallucinations commonly en... | {
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2502.14272 | Capturing Nuanced Preferences: Preference-Aligned Distillation for Small
Language Models | [
"cs.CL",
"cs.AI"
] | Aligning small language models (SLMs) with human values typically involves distilling preference knowledge from large language models (LLMs). However, existing distillation methods model preference knowledge in teacher LLMs by comparing pairwise responses, overlooking the extent of difference between responses. This li... | {
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2502.14273 | LLM-EvRep: Learning an LLM-Compatible Event Representation Using a
Self-Supervised Framework | [
"cs.CV",
"cs.AI",
"cs.MM"
] | Recent advancements in event-based recognition have demonstrated significant promise, yet most existing approaches rely on extensive training, limiting their adaptability for efficient processing of event-driven visual content. Meanwhile, large language models (LLMs) have exhibited remarkable zero-shot capabilities acr... | {
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2502.14275 | Fact or Guesswork? Evaluating Large Language Model's Medical Knowledge
with Structured One-Hop Judgment | [
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have been widely adopted in various downstream task domains. However, their ability to directly recall and apply factual medical knowledge remains under-explored. Most existing medical QA benchmarks assess complex reasoning or multi-hop inference, making it difficult to isolate LLMs' inhere... | {
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2502.14276 | STeCa: Step-level Trajectory Calibration for LLM Agent Learning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large language model (LLM)-based agents have shown promise in tackling complex tasks by interacting dynamically with the environment. Existing work primarily focuses on behavior cloning from expert demonstrations and preference learning through exploratory trajectory sampling. However, these methods often struggle in l... | {
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2502.14279 | OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from
Monocular Camera Images | [
"cs.CV"
] | Monocular depth estimation is a rudimentary task in robotic perception. Recently, with the development of more accurate and robust neural network models and different types of datasets, monocular depth estimation has significantly improved performance and efficiency. However, most of the research in this area focuses o... | {
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2502.14280 | EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts | [
"cs.CL",
"cs.AI"
] | Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks. However, efficient processing of long contexts using LLMs remains a significant challenge. We introduce \textbf{EpMAN} -- a method for processing long contexts in an \textit{episodic memory} module while \textit{ho... | {
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2502.14281 | Correcting Noisy Multilabel Predictions: Modeling Label Noise through
Latent Space Shifts | [
"cs.LG",
"cs.AI"
] | Noise in data appears to be inevitable in most real-world machine learning applications and would cause severe overfitting problems. Not only can data features contain noise, but labels are also prone to be noisy due to human input. In this paper, rather than noisy label learning in multiclass classifications, we inste... | {
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2502.14282 | PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex
Task Automation on PC | [
"cs.CV"
] | In the field of MLLM-based GUI agents, compared to smartphones, the PC scenario not only features a more complex interactive environment, but also involves more intricate intra- and inter-app workflows. To address these issues, we propose a hierarchical agent framework named PC-Agent. Specifically, from the perception ... | {
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2502.14285 | Vulnerability of Text-to-Image Models to Prompt Template Stealing: A
Differential Evolution Approach | [
"cs.CL"
] | Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates that can generate similar images. This work investigates a critical security vulnerability: attackers can steal prompt templates using only a ... | {
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2502.14289 | Drift: Decoding-time Personalized Alignments with Implicit User
Preferences | [
"cs.CL"
] | Personalized alignments for individual users have been a long-standing goal in large language models (LLMs). We introduce Drift, a novel framework that personalizes LLMs at decoding time with implicit user preferences. Traditional Reinforcement Learning from Human Feedback (RLHF) requires thousands of annotated example... | {
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2502.14293 | Graph Anomaly Detection via Adaptive Test-time Representation Learning
across Out-of-Distribution Domains | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Graph Anomaly Detection (GAD) has demonstrated great effectiveness in identifying unusual patterns within graph-structured data. However, while labeled anomalies are often scarce in emerging applications, existing supervised GAD approaches are either ineffective or not applicable when moved across graph domains due to ... | {
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2502.14294 | DAG: Deep Adaptive and Generative $K$-Free Community Detection on
Attributed Graphs | [
"cs.SI"
] | Community detection on attributed graphs with rich semantic and topological information offers great potential for real-world network analysis, especially user matching in online games. Graph Neural Networks (GNNs) have recently enabled Deep Graph Clustering (DGC) methods to learn cluster assignments from semantic and ... | {
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2502.14297 | An Evaluation of Sakana's AI Scientist for Autonomous Research: Wishful
Thinking or an Emerging Reality Towards 'Artificial General Research
Intelligence' (AGRI)? | [
"cs.IR",
"cs.AI",
"cs.LG"
] | A major step toward Artificial General Intelligence (AGI) and Super Intelligence is AI's ability to autonomously conduct research - what we term Artificial General Research Intelligence (AGRI). If machines could generate hypotheses, conduct experiments, and write research papers without human intervention, it would tra... | {
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2502.14298 | Generalization Certificates for Adversarially Robust Bayesian Linear
Regression | [
"cs.LG",
"stat.ML"
] | Adversarial robustness of machine learning models is critical to ensuring reliable performance under data perturbations. Recent progress has been on point estimators, and this paper considers distributional predictors. First, using the link between exponential families and Bregman divergences, we formulate an adversari... | {
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2502.14301 | SEA-HELM: Southeast Asian Holistic Evaluation of Language Models | [
"cs.CL",
"cs.AI"
] | With the rapid emergence of novel capabilities in Large Language Models (LLMs), the need for rigorous multilingual and multicultural benchmarks that are integrated has become more pronounced. Though existing LLM benchmarks are capable of evaluating specific capabilities of LLMs in English as well as in various mid- to ... | {
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2502.14302 | MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations
in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Advancements in Large Language Models (LLMs) and their increasing use in medical question-answering necessitate rigorous evaluation of their reliability. A critical challenge lies in hallucination, where models generate plausible yet factually incorrect outputs. In the medical domain, this poses serious risks to patien... | {
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2502.14305 | Efficient AI in Practice: Training and Deployment of Efficient LLMs for
Industry Applications | [
"cs.IR",
"cs.LG"
] | Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendations to generative tasks. Although scaling laws indicate that larger models generally yield better generalization and performance, their substantial computational requirements... | {
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2502.14307 | {\mu}RL: Discovering Transient Execution Vulnerabilities Using
Reinforcement Learning | [
"cs.CR",
"cs.AR",
"cs.LG"
] | We propose using reinforcement learning to address the challenges of discovering microarchitectural vulnerabilities, such as Spectre and Meltdown, which exploit subtle interactions in modern processors. Traditional methods like random fuzzing fail to efficiently explore the vast instruction space and often miss vulnera... | {
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2502.14309 | On Theoretical Limits of Learning with Label Differential Privacy | [
"cs.LG",
"cs.IT",
"math.IT"
] | Label differential privacy (DP) is designed for learning problems involving private labels and public features. While various methods have been proposed for learning under label DP, the theoretical limits remain largely unexplored. In this paper, we investigate the fundamental limits of learning with label DP in both l... | {
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2502.14311 | The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted
Decision-Making | [
"cs.HC",
"cs.CL",
"cs.CY"
] | In AI-assisted decision-making, it is crucial but challenging for humans to appropriately rely on AI, especially in high-stakes domains such as finance and healthcare. This paper addresses this problem from a human-centered perspective by presenting an intervention for self-confidence shaping, designed to calibrate sel... | {
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2502.14314 | ODVerse33: Is the New YOLO Version Always Better? A Multi Domain
benchmark from YOLO v5 to v11 | [
"cs.CV"
] | You Look Only Once (YOLO) models have been widely used for building real-time object detectors across various domains. With the increasing frequency of new YOLO versions being released, key questions arise. Are the newer versions always better than their previous versions? What are the core innovations in each YOLO ver... | {
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2502.14315 | Unveiling Cultural Blind Spots: Analyzing the Limitations of mLLMs in
Procedural Text Comprehension | [
"cs.CL"
] | Despite the impressive performance of multilingual large language models (mLLMs) in various natural language processing tasks, their ability to understand procedural texts, particularly those with culture-specific content, remains largely unexplored. Texts describing cultural procedures, including rituals, traditional ... | {
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2502.14316 | Textured 3D Regenerative Morphing with 3D Diffusion Prior | [
"cs.CV",
"cs.AI"
] | Textured 3D morphing creates smooth and plausible interpolation sequences between two 3D objects, focusing on transitions in both shape and texture. This is important for creative applications like visual effects in filmmaking. Previous methods rely on establishing point-to-point correspondences and determining smooth ... | {
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2502.14317 | ParallelComp: Parallel Long-Context Compressor for Length Extrapolation | [
"cs.CL"
] | Efficiently handling long contexts is crucial for large language models (LLMs). While rotary position embeddings (RoPEs) enhance length generalization, effective length extrapolation remains challenging and often requires costly fine-tuning. In contrast, recent training-free approaches suffer from the attention sink ph... | {
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2502.14318 | Line Goes Up? Inherent Limitations of Benchmarks for Evaluating Large
Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) regularly demonstrate new and impressive performance on a wide range of language, knowledge, and reasoning benchmarks. Such rapid progress has led many commentators to argue that LLM general cognitive capabilities have likewise rapidly improved, with the implication that such models are bec... | {
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2502.14321 | Beyond Self-Talk: A Communication-Centric Survey of LLM-Based
Multi-Agent Systems | [
"cs.MA",
"cs.CL"
] | Large Language Models (LLMs) have recently demonstrated remarkable capabilities in reasoning, planning, and decision-making. Building upon these strengths, researchers have begun incorporating LLMs into multi-agent systems (MAS), where agents collaborate or compete through natural language interactions to tackle tasks ... | {
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2502.14327 | ChemHTS: Hierarchical Tool Stacking for Enhancing Chemical Agents | [
"cs.CE"
] | Large Language Models (LLMs) have demonstrated remarkable potential in scientific research, particularly in chemistry-related tasks such as molecular design, reaction prediction, and property estimation. While tool-augmented LLMs have been introduced to enhance reasoning and computation in these domains, existing appro... | {
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2502.14332 | A Collaborative Jade Recognition System for Mobile Devices Based on
Lightweight and Large Models | [
"cs.CV",
"cs.IR"
] | With the widespread adoption and development of mobile devices, vision-based recognition applications have become a hot topic in research. Jade, as an important cultural heritage and artistic item, has significant applications in fields such as jewelry identification and cultural relic preservation. However, existing j... | {
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2502.14333 | A Survey on Feedback-based Multi-step Reasoning for Large Language
Models on Mathematics | [
"cs.CL",
"cs.AI"
] | Recent progress in large language models (LLM) found chain-of-thought prompting strategies to improve the reasoning ability of LLMs by encouraging problem solving through multiple steps. Therefore, subsequent research aimed to integrate the multi-step reasoning process into the LLM itself through process rewards as fee... | {
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2502.14334 | Purest Quantum State Identification | [
"quant-ph",
"cs.AI"
] | Precise identification of quantum states under noise constraints is essential for quantum information processing. In this study, we generalize the classical best arm identification problem to quantum domains, designing methods for identifying the purest one within $K$ unknown $n$-qubit quantum states using $N$ samples.... | {
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2502.14335 | Information Types in Product Reviews | [
"cs.CL"
] | Information in text is communicated in a way that supports a goal for its reader. Product reviews, for example, contain opinions, tips, product descriptions, and many other types of information that provide both direct insights, as well as unexpected signals for downstream applications. We devise a typology of 24 commu... | {
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2502.14338 | English Please: Evaluating Machine Translation for Multilingual Bug
Reports | [
"cs.CL",
"cs.SE"
] | Accurate translation of bug reports is critical for efficient collaboration in global software development. In this study, we conduct the first comprehensive evaluation of machine translation (MT) performance on bug reports, analyzing the capabilities of DeepL, AWS Translate, and ChatGPT using data from the Visual Stud... | {
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2502.14340 | Earlier Tokens Contribute More: Learning Direct Preference Optimization
From Temporal Decay Perspective | [
"cs.CL"
] | Direct Preference Optimization (DPO) has gained attention as an efficient alternative to reinforcement learning from human feedback (RLHF) for aligning large language models (LLMs) with human preferences. Despite its advantages, DPO suffers from a length bias, generating responses longer than those from the reference m... | {
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2502.14344 | Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive
Gradient Modulation Mechanism | [
"cs.CV"
] | Binary Spiking Neural Networks (BSNNs) inherit the eventdriven paradigm of SNNs, while also adopting the reduced storage burden of binarization techniques. These distinct advantages grant BSNNs lightweight and energy-efficient characteristics, rendering them ideal for deployment on resource-constrained edge devices. Ho... | {
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2502.14345 | FlowAgent: Achieving Compliance and Flexibility for Workflow Agents | [
"cs.AI"
] | The integration of workflows with large language models (LLMs) enables LLM-based agents to execute predefined procedures, enhancing automation in real-world applications. Traditional rule-based methods tend to limit the inherent flexibility of LLMs, as their predefined execution paths restrict the models' action space,... | {
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2502.14350 | Optimize Cardinality Estimation Model Pretraining by Simplifying the
Training Datasets | [
"cs.DB",
"cs.LG"
] | The cardinality estimation is a key aspect of query optimization research, and its performance has significantly improved with the integration of machine learning. To overcome the "cold start" problem or the lack of model transferability in learned cardinality estimators, some pre-training cardinality estimation models... | {
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2502.14351 | SegAnyPET: Universal Promptable Segmentation from Positron Emission
Tomography Images | [
"cs.CV"
] | Positron Emission Tomography (PET) imaging plays a crucial role in modern medical diagnostics by revealing the metabolic processes within a patient's body, which is essential for quantification of therapy response and monitoring treatment progress. However, the segmentation of PET images presents unique challenges due ... | {
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2502.14352 | SR-LLM: Rethinking the Structured Representation in Large Language Model | [
"cs.CL"
] | Structured representations, exemplified by Abstract Meaning Representation (AMR), have long been pivotal in computational linguistics. However, their role remains ambiguous in the Large Language Models (LLMs) era. Initial attempts to integrate structured representation into LLMs via a zero-shot setting yielded inferior... | {
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2502.14353 | Eliminating Majority Illusions | [
"cs.CC",
"cs.SI"
] | An opinion illusion refers to a phenomenon in social networks where agents may witness distributions of opinions among their neighbours that do not accurately reflect the true distribution of opinions in the population as a whole. A specific case of this occurs when there are only two possible choices, such as whether ... | {
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2502.14354 | Self-Improvement Towards Pareto Optimality: Mitigating Preference
Conflicts in Multi-Objective Alignment | [
"cs.LG",
"cs.CL"
] | Multi-Objective Alignment (MOA) aims to align LLMs' responses with multiple human preference objectives, with Direct Preference Optimization (DPO) emerging as a prominent approach. However, we find that DPO-based MOA approaches suffer from widespread preference conflicts in the data, where different objectives favor di... | {
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2502.14355 | Triply Laplacian Scale Mixture Modeling for Seismic Data Noise
Suppression | [
"cs.CV"
] | Sparsity-based tensor recovery methods have shown great potential in suppressing seismic data noise. These methods exploit tensor sparsity measures capturing the low-dimensional structures inherent in seismic data tensors to remove noise by applying sparsity constraints through soft-thresholding or hard-thresholding op... | {
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} |
2502.14356 | Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise
Rewards for Mathematical Reasoning | [
"cs.CL"
] | Direct Preference Optimization (DPO) often struggles with long-chain mathematical reasoning. Existing approaches, such as Step-DPO, typically improve this by focusing on the first erroneous step in the reasoning chain. However, they overlook all other steps and rely heavily on humans or GPT-4 to identify erroneous step... | {
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} |
2502.14358 | An exposition of recent list-size bounds of FRS Codes | [
"cs.CC",
"cs.IT",
"math.CO",
"math.IT"
] | In the last year, there have been some remarkable improvements in the combinatorial list-size bounds of Folded Reed Solomon codes and multiplicity codes. Starting from the work on Kopparty, Ron-Zewi, Saraf and Wootters (SIAM J. Comput. 2023) (and subsequent simplifications due to Tamo (IEEE Trans. Inform. Theory 2024),... | {
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} |
2502.14359 | Triangulating LLM Progress through Benchmarks, Games, and Cognitive
Tests | [
"cs.CL"
] | We examine three evaluation paradigms: large question-answering benchmarks (e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and cognitive tests (e.g., for working memory or theory of mind). First, we investigate which of the former two-benchmarks or games-is most effective at discriminating LL... | {
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} |
2502.14360 | Weed Detection using Convolutional Neural Network | [
"cs.CV"
] | In this paper we use convolutional neural networks (CNNs) for weed detection in agricultural land. We specifically investigate the application of two CNN layer types, Conv2d and dilated Conv2d, for weed detection in crop fields. The suggested method extracts features from the input photos using pre-trained models, whic... | {
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} |
2502.14361 | Retrieval-Augmented Process Reward Model for Generalizable Mathematical
Reasoning | [
"cs.AI",
"cs.IR"
] | While large language models (LLMs) have significantly advanced mathematical reasoning, Process Reward Models (PRMs) have been developed to evaluate the logical validity of reasoning steps. However, PRMs still struggle with out-of-distribution (OOD) challenges. This paper identifies key OOD issues, including step OOD, c... | {
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} |
2502.14363 | Topology-Aware Wavelet Mamba for Airway Structure Segmentation in
Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans | [
"eess.IV",
"cs.CV"
] | Nasopharyngeal carcinoma (NPC) patients often undergo radiotherapy and chemotherapy, which can lead to postoperative complications such as limited mouth opening and joint stiffness, particularly in recurrent cases that require re-surgery. These complications can affect airway function, making accurate postoperative air... | {
"Other": 0,
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} |
2502.14365 | Is Q-learning an Ill-posed Problem? | [
"cs.LG",
"cs.AI"
] | This paper investigates the instability of Q-learning in continuous environments, a challenge frequently encountered by practitioners. Traditionally, this instability is attributed to bootstrapping and regression model errors. Using a representative reinforcement learning benchmark, we systematically examine the effect... | {
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} |
2502.14366 | Entropy-UID: A Method for Optimizing Information Density | [
"cs.CL",
"cs.AI"
] | Balanced and efficient information flow is essential for optimizing language generation models. In this work, we propose Entropy-UID, a new token selection method that balances entropy and Uniform Information Density (UID) principles for enhanced efficiency of text generation. Our approach adaptively adjusts token sele... | {
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} |
2502.14370 | PPO-MI: Efficient Black-Box Model Inversion via Proximal Policy
Optimization | [
"cs.LG",
"cs.CV"
] | Model inversion attacks pose a significant privacy risk by attempting to reconstruct private training data from trained models. Most of the existing methods either depend on gradient estimation or require white-box access to model parameters, which limits their applicability in practical scenarios. In this paper, we pr... | {
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"cs.SY": 0
} |
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