id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.16312 | LinPrim: Linear Primitives for Differentiable Volumetric Rendering | [
"cs.CV"
] | Volumetric rendering has become central to modern novel view synthesis methods, which use differentiable rendering to optimize 3D scene representations directly from observed views. While many recent works build on NeRF or 3D Gaussians, we explore an alternative volumetric scene representation. More specifically, we in... | {
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2501.16319 | Adaptive Iterative Compression for High-Resolution Files: an Approach
Focused on Preserving Visual Quality in Cinematic Workflows | [
"cs.CV",
"cs.ET",
"cs.LG",
"cs.PF"
] | This study presents an iterative adaptive compression model for high-resolution DPX-derived TIFF files used in cinematographic workflows and digital preservation. The model employs SSIM and PSNR metrics to dynamically adjust compression parameters across three configurations (C0, C1, C2), achieving storage reductions u... | {
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2501.16322 | Implicit Bias in Matrix Factorization and its Explicit Realization in a
New Architecture | [
"cs.LG",
"math.OC",
"stat.ML"
] | Gradient descent for matrix factorization is known to exhibit an implicit bias toward approximately low-rank solutions. While existing theories often assume the boundedness of iterates, empirically the bias persists even with unbounded sequences. We thus hypothesize that implicit bias is driven by divergent dynamics ma... | {
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2501.16325 | Tailored Forecasting from Short Time Series via Meta-learning | [
"cs.LG",
"nlin.CD",
"physics.comp-ph"
] | Machine learning (ML) models can be effective for forecasting the dynamics of unknown systems from time-series data, but they often require large amounts of data and struggle to generalize across systems with varying dynamics. Combined, these issues make forecasting from short time series particularly challenging. To a... | {
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2501.16327 | LUCY: Linguistic Understanding and Control Yielding Early Stage of Her | [
"cs.CL",
"cs.SD",
"eess.AS"
] | The film Her features Samantha, a sophisticated AI audio agent who is capable of understanding both linguistic and paralinguistic information in human speech and delivering real-time responses that are natural, informative and sensitive to emotional subtleties. Moving one step toward more sophisticated audio agent from... | {
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2501.16329 | sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for
Automatic Sleep Staging | [
"cs.LG",
"cs.AI"
] | Automatic sleep staging based on electroencephalography (EEG) and electromyography (EMG) signals is an important aspect of sleep-related research. Current sleep staging methods suffer from two major drawbacks. First, there are limited information interactions between modalities in the existing methods. Second, current ... | {
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2501.16330 | RelightVid: Temporal-Consistent Diffusion Model for Video Relighting | [
"cs.CV",
"cs.AI"
] | Diffusion models have demonstrated remarkable success in image generation and editing, with recent advancements enabling albedo-preserving image relighting. However, applying these models to video relighting remains challenging due to the lack of paired video relighting datasets and the high demands for output fidelity... | {
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2501.16331 | Decoding OTC Government Bond Market Liquidity: An ABM Model for Market
Dynamics | [
"q-fin.TR",
"cs.AI"
] | The over-the-counter (OTC) government bond markets are characterised by their bilateral trading structures, which pose unique challenges to understanding and ensuring market stability and liquidity. In this paper, we develop a bespoke ABM that simulates market-maker interactions within a stylised government bond market... | {
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2501.16333 | A New Proof for the Linear Filtering and Smoothing Equations, and
Asymptotic Expansion of Nonlinear Filtering | [
"eess.SP",
"cs.IT",
"math.IT",
"math.PR",
"math.ST",
"stat.TH"
] | In this paper, we propose a new approach to the linear filtering and smoothing problem and demonstrate its applicability to nonlinear filtering. For the linear case, our main theorem provides an explicit expression for the conditional distribution of the hidden process given the observations, leading to a novel derivat... | {
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2501.16334 | RNN-Based Models for Predicting Seizure Onset in Epileptic Patients | [
"eess.SP",
"cs.LG"
] | Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel Recurrent Neural Network (RNN)-based method fo... | {
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2501.16336 | Runtime Analysis of Evolutionary Algorithms for Multiparty
Multiobjective Optimization | [
"cs.NE",
"cs.AI"
] | In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem (e.g., bargaining games), the problem can be modeled as a multi-party multi-objective optimization problem (MPMOP). While numerous evolutionary algorithms have been propose... | {
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2501.16337 | Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient
Neuromorphic Computing | [
"cs.NE",
"cs.AI",
"cs.AR",
"cs.LG"
] | The recent rise of Large Language Models (LLMs) has revolutionized the deep learning field. However, the desire to deploy LLMs on edge devices introduces energy efficiency and latency challenges. Recurrent LLM (R-LLM) architectures have proven effective in mitigating the quadratic complexity of self-attention, making t... | {
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2501.16341 | Developing Enhanced Conversational Agents for Social Virtual Worlds | [
"eess.AS",
"cs.CL",
"cs.SD"
] | In this paper, we present a methodology for the development of embodied conversational agents for social virtual worlds. The agents provide multimodal communication with their users in which speech interaction is included. Our proposal combines different techniques related to Artificial Intelligence, Natural Language P... | {
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2501.16343 | Self-orthogonal and self-dual codes from maximal curves | [
"cs.IT",
"math.AG",
"math.IT"
] | In the field of algebraic geometric codes (AG codes), the characterization of dual codes has long been a challenging problem which relies on differentials. In this paper, we provide some descriptions for certain differentials utilizing algebraic structure of finite fields and geometric properties of algebraic curves. M... | {
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2501.16344 | WhiSPA: Semantically and Psychologically Aligned Whisper with
Self-Supervised Contrastive and Student-Teacher Learning | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.SD"
] | Current speech encoding pipelines often rely on an additional text-based LM to get robust representations of human communication, even though SotA speech-to-text models often have a LM within. This work proposes an approach to improve the LM within an audio model such that the subsequent text-LM is unnecessary. We intr... | {
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2501.16345 | Self-Clustering Graph Transformer Approach to Model Resting-State
Functional Brain Activity | [
"cs.LG",
"cs.AI"
] | Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes, as it allows for the functional organization of the brain to be captured without... | {
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2501.16346 | Self-supervised Graph Transformer with Contrastive Learning for Brain
Connectivity Analysis towards Improving Autism Detection | [
"cs.LG",
"cs.AI"
] | Functional Magnetic Resonance Imaging (fMRI) provides useful insights into the brain function both during task or rest. Representing fMRI data using correlation matrices is found to be a reliable method of analyzing the inherent connectivity of the brain in the resting and active states. Graph Neural Networks (GNNs) ha... | {
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2501.16347 | Identification of Hardware Trojan Locations in Gate-Level Netlist using
Nearest Neighbour Approach integrated with Machine Learning Technique | [
"cs.LG",
"cs.AI"
] | In the evolving landscape of integrated circuit design, detecting Hardware Trojans (HTs) within a multi entity based design cycle presents significant challenges. This research proposes an innovative machine learning-based methodology for identifying malicious logic gates in gate-level netlists. By focusing on path ret... | {
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2501.16348 | An Integrated Approach to AI-Generated Content in e-health | [
"cs.LG",
"cs.AI"
] | Artificial Intelligence-Generated Content, a subset of Generative Artificial Intelligence, holds significant potential for advancing the e-health sector by generating diverse forms of data. In this paper, we propose an end-to-end class-conditioned framework that addresses the challenge of data scarcity in health applic... | {
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2501.16349 | Risk-Informed Diffusion Transformer for Long-Tail Trajectory Prediction
in the Crash Scenario | [
"cs.LG",
"cs.AI"
] | Trajectory prediction methods have been widely applied in autonomous driving technologies. Although the overall performance accuracy of trajectory prediction is relatively high, the lack of trajectory data in critical scenarios in the training data leads to the long-tail phenomenon. Normally, the trajectories of the ta... | {
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2501.16350 | A Method for Multi-Hop Question Answering on Persian Knowledge Graph | [
"cs.IR",
"cs.AI",
"cs.CL"
] | Question answering systems are the latest evolution in information retrieval technology, designed to accept complex queries in natural language and provide accurate answers using both unstructured and structured knowledge sources. Knowledge Graph Question Answering (KGQA) systems fulfill users' information needs by uti... | {
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2501.16352 | Mixture of Experts (MoE): A Big Data Perspective | [
"cs.LG",
"cs.AI"
] | As the era of big data arrives, traditional artificial intelligence algorithms have difficulty processing the demands of massive and diverse data. Mixture of experts (MoE) has shown excellent performance and broad application prospects. This paper provides an in-depth review and analysis of the latest progress in this ... | {
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2501.16353 | Synthetic Data Generation by Supervised Neural Gas Network for
Physiological Emotion Recognition Data | [
"cs.NE",
"cs.AI",
"cs.LG",
"eess.SP"
] | Data scarcity remains a significant challenge in the field of emotion recognition using physiological signals, as acquiring comprehensive and diverse datasets is often prevented by privacy concerns and logistical constraints. This limitation restricts the development and generalization of robust emotion recognition mod... | {
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2501.16354 | Adaptive Hoeffding Tree with Transfer Learning for Streaming
Synchrophasor Data Sets | [
"cs.LG",
"cs.AI"
] | Synchrophasor technology or phasor measurement units (PMUs) are known to detect multiple type of oscillations or faults better than Supervisory Control and Data Acquisition (SCADA) systems, but the volume of Bigdata (e.g., 30-120 samples per second on a single PMU) generated by these sensors at the aggregator level (e.... | {
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2501.16355 | How Strategic Agents Respond: Comparing Analytical Models with
LLM-Generated Responses in Strategic Classification | [
"cs.LG",
"cs.AI"
] | When machine learning (ML) algorithms are used to automate human-related decisions, human agents may gain knowledge of the decision policy and behave strategically to obtain desirable outcomes. Strategic Classification (SC) has been proposed to address the interplay between agents and decision-makers. Prior work on SC ... | {
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2501.16356 | Evaluating Binary Decision Biases in Large Language Models: Implications
for Fair Agent-Based Financial Simulations | [
"cs.LG",
"cs.AI"
] | Large Language Models (LLMs) are increasingly being used to simulate human-like decision making in agent-based financial market models (ABMs). As models become more powerful and accessible, researchers can now incorporate individual LLM decisions into ABM environments. However, integration may introduce inherent biases... | {
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2501.16357 | EVolutionary Independent DEtermiNistiC Explanation | [
"cs.LG",
"cs.AI",
"eess.SP"
] | The widespread use of artificial intelligence deep neural networks in fields such as medicine and engineering necessitates understanding their decision-making processes. Current explainability methods often produce inconsistent results and struggle to highlight essential signals influencing model inferences. This paper... | {
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2501.16358 | The OpenLAM Challenges | [
"cs.LG",
"cond-mat.mtrl-sci",
"physics.comp-ph"
] | Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep Potential team has been actively pretraining LAMs and launched the OpenLAM Initiative to develop an open-source foundation model spanning t... | {
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2501.16360 | Momentum Contrastive Learning with Enhanced Negative Sampling and Hard
Negative Filtering | [
"cs.LG",
"cs.AI"
] | Contrastive learning has become pivotal in unsupervised representation learning, with frameworks like Momentum Contrast (MoCo) effectively utilizing large negative sample sets to extract discriminative features. However, traditional approaches often overlook the full potential of key embeddings and are susceptible to p... | {
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2501.16361 | Large Language Models Meet Graph Neural Networks for Text-Numeric Graph
Reasoning | [
"cs.LG",
"cs.AI"
] | In real-world scientific discovery, human beings always make use of the accumulated prior knowledge with imagination pick select one or a few most promising hypotheses from large and noisy data analysis results. In this study, we introduce a new type of graph structure, the text-numeric graph (TNG), which is defined as... | {
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2501.16362 | A novel Trunk Branch-net PINN for flow and heat transfer prediction in
porous medium | [
"cs.LG",
"physics.flu-dyn"
] | A novel Trunk-Branch (TB)-net physics-informed neural network (PINN) architecture is developed, which is a PINN-based method incorporating trunk and branch nets to capture both global and local features. The aim is to solve four main classes of problems: forward flow problem, forward heat transfer problem, inverse heat... | {
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2501.16364 | Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained
Intra- and Inter-Variate Dependencies | [
"cs.LG",
"cs.AI"
] | Multivariate time series anomaly detection is essential for failure management in web application operations, as it directly influences the effectiveness and timeliness of implementing remedial or preventive measures. This task is often framed as a semi-supervised learning problem, where only normal data are available ... | {
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2501.16365 | CAND: Cross-Domain Ambiguity Inference for Early Detecting Nuanced
Illness Deterioration | [
"cs.LG",
"cs.AI"
] | Early detection of patient deterioration is essential for timely treatment, with vital signs like heart rates being key health indicators. Existing methods tend to solely analyze vital sign waveforms, ignoring transition relationships of waveforms within each vital sign and the correlation strengths among various vital... | {
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2501.16368 | Foundation Models for CPS-IoT: Opportunities and Challenges | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY"
] | Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things (IoT), replacing mechanistic and basic statistical models with those derived from data. However, the first generation of ML approaches, whi... | {
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2501.16369 | Blockchain-based Crowdsourced Deep Reinforcement Learning as a Service | [
"cs.LG",
"cs.AI"
] | Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-... | {
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2501.16370 | Advanced Physics-Informed Neural Network with Residuals for Solving
Complex Integral Equations | [
"cs.LG",
"cs.AI",
"cs.NA",
"cs.NE",
"math.NA"
] | In this paper, we present the Residual Integral Solver Network (RISN), a novel neural network architecture designed to solve a wide range of integral and integro-differential equations, including one-dimensional, multi-dimensional, ordinary and partial integro-differential, systems, and fractional types. RISN integrate... | {
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2501.16371 | Which Optimizer Works Best for Physics-Informed Neural Networks and
Kolmogorov-Arnold Networks? | [
"cs.LG",
"cs.AI",
"math.OC"
] | Physics-Informed Neural Networks (PINNs) have revolutionized the computation of PDE solutions by integrating partial differential equations (PDEs) into the neural network's training process as soft constraints, becoming an important component of the scientific machine learning (SciML) ecosystem. In its current implemen... | {
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2501.16372 | Low-Rank Adapters Meet Neural Architecture Search for LLM Compression | [
"cs.LG",
"cs.AI",
"cs.CL"
] | The rapid expansion of Large Language Models (LLMs) has posed significant challenges regarding the computational resources required for fine-tuning and deployment. Recent advancements in low-rank adapters have demonstrated their efficacy in parameter-efficient fine-tuning (PEFT) of these models. This retrospective pape... | {
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2501.16373 | Unveiling Discrete Clues: Superior Healthcare Predictions for Rare
Diseases | [
"cs.LG",
"cs.AI",
"cs.CE"
] | Accurate healthcare prediction is essential for improving patient outcomes. Existing work primarily leverages advanced frameworks like attention or graph networks to capture the intricate collaborative (CO) signals in electronic health records. However, prediction for rare diseases remains challenging due to limited co... | {
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2501.16374 | SAFR: Neuron Redistribution for Interpretability | [
"cs.LG",
"cs.AI"
] | Superposition refers to encoding representations of multiple features within a single neuron, which is common in deep neural networks. This property allows neurons to combine and represent multiple features, enabling the model to capture intricate information and handle complex tasks. Despite promising performance, the... | {
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2501.16375 | On Storage Neural Network Augmented Approximate Nearest Neighbor Search | [
"cs.LG",
"cs.AI",
"cs.IR"
] | Large-scale approximate nearest neighbor search (ANN) has been gaining attention along with the latest machine learning researches employing ANNs. If the data is too large to fit in memory, it is necessary to search for the most similar vectors to a given query vector from the data stored in storage devices, not from t... | {
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2501.16376 | HWPQ: Hessian-free Weight Pruning-Quantization For LLM Compression And
Acceleration | [
"cs.LG",
"cs.AI"
] | Large Language Models (LLMs) have achieved remarkable success across numerous domains. However, the high time complexity of existing pruning and quantization methods significantly hinders their effective deployment on resource-constrained consumer or edge devices. In this study, we propose a novel Hessian-free Weight P... | {
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2501.16377 | Optimal Signal Decomposition-based Multi-Stage Learning for Battery
Health Estimation | [
"cs.LG",
"cs.AI"
] | Battery health estimation is fundamental to ensure battery safety and reduce cost. However, achieving accurate estimation has been challenging due to the batteries' complex nonlinear aging patterns and capacity regeneration phenomena. In this paper, we propose OSL, an optimal signal decomposition-based multi-stage mach... | {
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2501.16378 | Internal Activation Revision: Safeguarding Vision Language Models
Without Parameter Update | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | Vision-language models (VLMs) demonstrate strong multimodal capabilities but have been found to be more susceptible to generating harmful content compared to their backbone large language models (LLMs). Our investigation reveals that the integration of images significantly shifts the model's internal activations during... | {
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2501.16379 | FedAGHN: Personalized Federated Learning with Attentive Graph
HyperNetworks | [
"cs.LG",
"cs.AI"
] | Personalized Federated Learning (PFL) aims to address the statistical heterogeneity of data across clients by learning the personalized model for each client. Among various PFL approaches, the personalized aggregation-based approach conducts parameter aggregation in the server-side aggregation phase to generate persona... | {
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2501.16380 | UDiTQC: U-Net-Style Diffusion Transformer for Quantum Circuit Synthesis | [
"cs.LG",
"cs.AI",
"quant-ph"
] | Quantum computing is a transformative technology with wide-ranging applications, and efficient quantum circuit generation is crucial for unlocking its full potential. Current diffusion model approaches based on U-Net architectures, while promising, encounter challenges related to computational efficiency and modeling g... | {
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2501.16381 | Reduced-order modeling and classification of hydrodynamic pattern
formation in gravure printing | [
"cs.LG",
"physics.flu-dyn"
] | Hydrodynamic pattern formation phenomena in printing and coating processes are still not fully understood. However, fundamental understanding is essential to achieve high-quality printed products and to tune printed patterns according to the needs of a specific application like printed electronics, graphical printing, ... | {
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2501.16382 | GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale
Protein-protein Interaction Exploration | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Drug discovery (DD) has tremendously contributed to maintaining and improving public health. Hypothesizing that inhibiting protein misfolding can slow disease progression, researchers focus on target identification (Target ID) to find protein structures for drug binding. While Large Language Models (LLMs) and Retrieval... | {
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2501.16383 | RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via
Outlier-Aware Adaptive Rotations | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Key-Value (KV) cache facilitates efficient large language models (LLMs) inference by avoiding recomputation of past KVs. As the batch size and context length increase, the oversized KV caches become a significant memory bottleneck, highlighting the need for efficient compression. Existing KV quantization rely on fine-g... | {
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2501.16384 | MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate
Selective State Space Modeling | [
"eess.SP",
"cs.LG"
] | Point cloud enhancement is the process of generating a high-quality point cloud from an incomplete input. This is done by filling in the missing details from a reference like the ground truth via regression, for example. In addition to unimodal image and point cloud reconstruction, we focus on the task of view-guided p... | {
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2501.16385 | FBQuant: FeedBack Quantization for Large Language Models | [
"cs.LG",
"cs.CL"
] | Deploying Large Language Models (LLMs) on edge devices is increasingly important, as it eliminates reliance on network connections, reduces expensive API calls, and enhances user privacy. However, on-device deployment is challenging due to the limited computational resources of edge devices. In particular, the key bott... | {
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2501.16386 | ILETIA: An AI-enhanced method for individualized trigger-oocyte pickup
interval estimation of progestin-primed ovarian stimulation protocol | [
"q-bio.QM",
"cs.LG"
] | In vitro fertilization-embryo transfer (IVF-ET) stands as one of the most prevalent treatments for infertility. During an IVF-ET cycle, the time interval between trigger shot and oocyte pickup (OPU) is a pivotal period for follicular maturation, which determines mature oocytes yields and impacts the success of subseque... | {
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2501.16388 | Development and Validation of a Dynamic Kidney Failure Prediction Model
based on Deep Learning: A Real-World Study with External Validation | [
"cs.LG",
"stat.AP"
] | Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem. At present, most of the models used for predicting the progression of CKD are static models. We aim to develop a dynamic kidney failure prediction model based on deep... | {
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2501.16389 | Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor
Policy Transfer | [
"cs.RO",
"cs.CV"
] | Simulation offers a scalable and efficient alternative to real-world data collection for learning visuomotor robotic policies. However, the simulation-to-reality, or "Sim2Real" distribution shift -- introduced by employing simulation-trained policies in real-world environments -- frequently prevents successful policy t... | {
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2501.16391 | Leveraging Induced Transferable Binding Principles for Associative
Prediction of Novel Drug-Target Interactions | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | Significant differences in protein structures hinder the generalization of existing drug-target interaction (DTI) models, which often rely heavily on pre-learned binding principles or detailed annotations. In contrast, BioBridge designs an Inductive-Associative pipeline inspired by the workflow of scientists who base t... | {
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2501.16392 | HMCGeo: IP Region Prediction Based on Hierarchical Multi-label
Classification | [
"cs.LG"
] | Fine-grained IP geolocation plays a critical role in applications such as location-based services and cybersecurity. Most existing fine-grained IP geolocation methods are regression-based; however, due to noise in the input data, these methods typically encounter kilometer-level prediction errors and provide incorrect ... | {
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2501.16393 | Improving Network Threat Detection by Knowledge Graph, Large Language
Model, and Imbalanced Learning | [
"cs.LG",
"cs.CR",
"stat.ML"
] | Network threat detection has been challenging due to the complexities of attack activities and the limitation of historical threat data to learn from. To help enhance the existing practices of using analytics, machine learning, and artificial intelligence methods to detect the network threats, we propose an integrated ... | {
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2501.16394 | Transformer^-1: Input-Adaptive Computation for Resource-Constrained
Deployment | [
"cs.LG"
] | Addressing the resource waste caused by fixed computation paradigms in deep learning models under dynamic scenarios, this paper proposes a Transformer$^{-1}$ architecture based on the principle of deep adaptivity. This architecture achieves dynamic matching between input features and computational resources by establis... | {
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2501.16396 | TopoNets: High Performing Vision and Language Models with Brain-Like
Topography | [
"cs.LG",
"cs.NE",
"q-bio.NC"
] | Neurons in the brain are organized such that nearby cells tend to share similar functions. AI models lack this organization, and past efforts to introduce topography have often led to trade-offs between topography and task performance. In this work, we present TopoLoss, a new loss function that promotes spatially organ... | {
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2501.16397 | THOR: A Generic Energy Estimation Approach for On-Device Training | [
"cs.LG"
] | Battery-powered mobile devices (e.g., smartphones, AR/VR glasses, and various IoT devices) are increasingly being used for AI training due to their growing computational power and easy access to valuable, diverse, and real-time data. On-device training is highly energy-intensive, making accurate energy consumption esti... | {
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2501.16398 | Visualizing the Local Atomic Environment Features of Machine Learning
Interatomic Potential | [
"cs.LG",
"physics.atom-ph"
] | This paper addresses the challenges of creating efficient and high-quality datasets for machine learning potential functions. We present a novel approach, termed DV-LAE (Difference Vectors based on Local Atomic Environments), which utilizes the properties of atomic local environments and employs histogram statistics to... | {
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2501.16399 | Detecting clinician implicit biases in diagnoses using proximal causal
inference | [
"cs.LG",
"stat.AP"
] | Clinical decisions to treat and diagnose patients are affected by implicit biases formed by racism, ableism, sexism, and other stereotypes. These biases reflect broader systemic discrimination in healthcare and risk marginalizing already disadvantaged groups. Existing methods for measuring implicit biases require contr... | {
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2501.16403 | Is Open Source the Future of AI? A Data-Driven Approach | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) have become central in academia and industry, raising concerns about privacy, transparency, and misuse. A key issue is the trustworthiness of proprietary models, with open-sourcing often proposed as a solution. However, open-sourcing presents challenges, including potential misuse, financia... | {
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2501.16404 | DynaPrompt: Dynamic Test-Time Prompt Tuning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Test-time prompt tuning enhances zero-shot generalization of vision-language models but tends to ignore the relatedness among test samples during inference. Online test-time prompt tuning provides a simple way to leverage the information in previous test samples, albeit with the risk of prompt collapse due to error acc... | {
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2501.16405 | DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine
Learning | [
"q-bio.GN",
"cs.LG"
] | Background: Phage therapy shows promise for treating antibiotic-resistant Klebsiella infections. Identifying phage depolymerases that target Klebsiella capsular polysaccharides is crucial, as these capsules contribute to biofilm formation and virulence. However, homology-based searches have limitations in novel depolym... | {
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2501.16409 | Classification of Mild Cognitive Impairment Based on Dynamic Functional
Connectivity Using Spatio-Temporal Transformer | [
"eess.IV",
"cs.AI",
"q-bio.NC"
] | Dynamic functional connectivity (dFC) using resting-state functional magnetic resonance imaging (rs-fMRI) is an advanced technique for capturing the dynamic changes of neural activities, and can be very useful in the studies of brain diseases such as Alzheimer's disease (AD). Yet, existing studies have not fully levera... | {
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2501.16410 | DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain
Segmentation | [
"cs.CV"
] | Current unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains. This assumption ignores the label-level domain gap, which is common in real-world scenarios, thus limiting their ability to identify finer-grained or novel catego... | {
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2501.16411 | PhysBench: Benchmarking and Enhancing Vision-Language Models for
Physical World Understanding | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.RO"
] | Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. While Vision-Language Models (VLMs) have shown great promise in reasoning and task planning for embodied agents, their ability to comprehend ph... | {
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2501.16443 | Objects matter: object-centric world models improve reinforcement
learning in visually complex environments | [
"cs.LG",
"cs.CV"
] | Deep reinforcement learning has achieved remarkable success in learning control policies from pixels across a wide range of tasks, yet its application remains hindered by low sample efficiency, requiring significantly more environment interactions than humans to reach comparable performance. Model-based reinforcement l... | {
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2501.16448 | What is Harm? Baby Don't Hurt Me! On the Impossibility of Complete Harm
Specification in AI Alignment | [
"cs.AI",
"cs.LG"
] | "First, do no harm" faces a fundamental challenge in artificial intelligence: how can we specify what constitutes harm? While prior work treats harm specification as a technical hurdle to be overcome through better algorithms or more data, we argue this assumption is unsound. Drawing on information theory, we demonstra... | {
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2501.16450 | 360Brew: A Decoder-only Foundation Model for Personalized Ranking and
Recommendation | [
"cs.IR",
"cs.AI"
] | Ranking and recommendation systems are the foundation for numerous online experiences, ranging from search results to personalized content delivery. These systems have evolved into complex, multilayered architectures that leverage vast datasets and often incorporate thousands of predictive models. The maintenance and e... | {
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2501.16453 | Detecting Zero-Day Attacks in Digital Substations via In-Context
Learning | [
"cs.LG",
"cs.AI"
] | The occurrences of cyber attacks on the power grids have been increasing every year, with novel attack techniques emerging every year. In this paper, we address the critical challenge of detecting novel/zero-day attacks in digital substations that employ the IEC-61850 communication protocol. While many heuristic and ma... | {
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2501.16456 | CoCoNUT: Structural Code Understanding does not fall out of a tree | [
"cs.LG",
"cs.SE"
] | Large Language Models (LLMs) have shown impressive performance across a wide array of tasks involving both structured and unstructured textual data. Recent results on various benchmarks for code generation, repair, or completion suggest that certain models have programming abilities comparable to or even surpass humans... | {
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2501.16458 | BiFold: Bimanual Cloth Folding with Language Guidance | [
"cs.RO",
"cs.CV"
] | Cloth folding is a complex task due to the inevitable self-occlusions of clothes, their complicated dynamics, and the disparate materials, geometries, and textures that garments can have. In this work, we learn folding actions conditioned on text commands. Translating high-level, abstract instructions into precise robo... | {
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2501.16466 | On the Feasibility of Using LLMs to Execute Multistage Network Attacks | [
"cs.CR",
"cs.AI"
] | LLMs have shown preliminary promise in some security tasks and CTF challenges. However, it is unclear whether LLMs are able to realize multistage network attacks, which involve executing a wide variety of actions across multiple hosts such as conducting reconnaissance, exploiting vulnerabilities to gain initial access,... | {
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2501.16467 | Cross-Domain Semantic Segmentation with Large Language Model-Assisted
Descriptor Generation | [
"cs.CV"
] | Semantic segmentation plays a crucial role in enabling machines to understand and interpret visual scenes at a pixel level. While traditional segmentation methods have achieved remarkable success, their generalization to diverse scenes and unseen object categories remains limited. Recent advancements in large language ... | {
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2501.16469 | Object Detection for Medical Image Analysis: Insights from the RT-DETR
Model | [
"cs.CV",
"cs.LG"
] | Deep learning has emerged as a transformative approach for solving complex pattern recognition and object detection challenges. This paper focuses on the application of a novel detection framework based on the RT-DETR model for analyzing intricate image data, particularly in areas such as diabetic retinopathy detection... | {
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2501.16471 | SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding
from Movie-Watching Experiments | [
"cs.LG",
"cs.AI",
"eess.AS",
"eess.IV",
"q-bio.NC"
] | Current AI frameworks for brain decoding and encoding, typically train and test models within the same datasets. This limits their utility for brain computer interfaces (BCI) or neurofeedback, for which it would be useful to pool experiences across individuals to better simulate stimuli not sampled during training. A k... | {
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} |
2501.16473 | Sensitivity Analysis of the Laser Power Control System to Measurement
Noise in SLS 3D Printers | [
"eess.SY",
"cs.SY"
] | Uniform temperature distribution in Selective Laser Sintering (SLS) is essential for producing durable 3D prints. Achieving uniformity requires a laser power control system that minimises deviation of the printing temperatures from the target temperature. Because the estimate of the actual process temperature is an inp... | {
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2501.16476 | Closed-Form Feedback-Free Learning with Forward Projection | [
"cs.LG",
"stat.ML"
] | State-of-the-art methods for backpropagation-free learning employ local error feedback to direct iterative optimisation via gradient descent. In this study, we examine the more restrictive setting where retrograde communication from neuronal outputs is unavailable for pre-synaptic weight optimisation. To address this c... | {
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} |
2501.16480 | Modular Framework for Uncertainty Prediction in Autonomous Vehicle
Motion Forecasting within Complex Traffic Scenarios | [
"cs.RO",
"cs.LG",
"eess.SP"
] | We propose a modular modeling framework designed to enhance the capture and validation of uncertainty in autonomous vehicle (AV) trajectory prediction. Departing from traditional deterministic methods, our approach employs a flexible, end-to-end differentiable probabilistic encoder-decoder architecture. This modular de... | {
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2501.16481 | Generating customized prompts for Zero-Shot Rare Event Medical Image
Classification using LLM | [
"cs.CV"
] | Rare events, due to their infrequent occurrences, do not have much data, and hence deep learning techniques fail in estimating the distribution for such data. Open-vocabulary models represent an innovative approach to image classification. Unlike traditional models, these models classify images into any set of categori... | {
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2501.16485 | Enhanced Position Estimation in Tactile Internet-Enabled Remote Robotic
Surgery Using MOESP-Based Kalman Filter | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Accurately estimating the position of a patient's side robotic arm in real time during remote surgery is a significant challenge, especially within Tactile Internet (TI) environments. This paper presents a new and efficient method for position estimation using a Kalman Filter (KF) combined with the Multivariable Output... | {
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2501.16487 | Network Risk Estimation: A Risk Estimation Paradigm for Cyber Networks | [
"eess.SY",
"cs.SY"
] | Cyber networks are fundamental to many organization's infrastructure, and the size of cyber networks is increasing rapidly. Risk measurement of the entities/endpoints that make up the network via available knowledge about possible threats has been the primary tool in cyber network security. However, the dynamic behavio... | {
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2501.16489 | Nonparametric Sparse Online Learning of the Koopman Operator | [
"stat.ML",
"cs.LG",
"cs.SY",
"eess.SY"
] | The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics. In this paper, we study the Koopman operator via its action on th... | {
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2501.16490 | Towards Robust Stability Prediction in Smart Grids: GAN-based Approach
under Data Constraints and Adversarial Challenges | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Smart grids are critical for addressing the growing energy demand due to global population growth and urbanization. They enhance efficiency, reliability, and sustainability by integrating renewable energy. Ensuring their availability and safety requires advanced operational control and safety measures. Researchers empl... | {
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} |
2501.16496 | Open Problems in Mechanistic Interpretability | [
"cs.LG"
] | Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in this field thus promises to provide greater assurance over AI system behavior and shed light on exciting scientific question... | {
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2501.16497 | Smoothed Embeddings for Robust Language Models | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CR",
"stat.ML"
] | Improving the safety and reliability of large language models (LLMs) is a crucial aspect of realizing trustworthy AI systems. Although alignment methods aim to suppress harmful content generation, LLMs are often still vulnerable to jailbreaking attacks that employ adversarial inputs that subvert alignment and induce ha... | {
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} |
2501.16504 | Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR
Inference | [
"eess.SP",
"cs.AI"
] | This paper investigates the significance of designing a reliable, intelligent, and true physical environment-aware precoding scheme by leveraging an accurately designed channel twin model to obtain realistic channel state information (CSI) for cellular communication systems. Specifically, we propose a fine-tuned multi-... | {
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} |
2501.16507 | Characterizing Network Structure of Anti-Trans Actors on TikTok | [
"cs.HC",
"cs.AI",
"cs.SI"
] | The recent proliferation of short form video social media sites such as TikTok has been effectively utilized for increased visibility, communication, and community connection amongst trans/nonbinary creators online. However, these same platforms have also been exploited by right-wing actors targeting trans/nonbinary pe... | {
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} |
2501.16509 | Reinforcement Learning for Quantum Circuit Design: Using Matrix
Representations | [
"quant-ph",
"cs.AI"
] | Quantum computing promises advantages over classical computing. The manufacturing of quantum hardware is in the infancy stage, called the Noisy Intermediate-Scale Quantum (NISQ) era. A major challenge is automated quantum circuit design that map a quantum circuit to gates in a universal gate set. In this paper, we pres... | {
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} |
2501.16510 | Decrypting the temperature field in flow boiling with latent diffusion
models | [
"physics.flu-dyn",
"cs.AI"
] | This paper presents an innovative method using Latent Diffusion Models (LDMs) to generate temperature fields from phase indicator maps. By leveraging the BubbleML dataset from numerical simulations, the LDM translates phase field data into corresponding temperature distributions through a two-stage training process inv... | {
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} |
2501.16513 | Deception in LLMs: Self-Preservation and Autonomous Goals in Large
Language Models | [
"cs.CL"
] | Recent advances in Large Language Models (LLMs) have incorporated planning and reasoning capabilities, enabling models to outline steps before execution and provide transparent reasoning paths. This enhancement has reduced errors in mathematical and logical tasks while improving accuracy. These developments have facili... | {
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} |
2501.16516 | How well can LLMs Grade Essays in Arabic? | [
"cs.CL",
"cs.AI"
] | This research assesses the effectiveness of state-of-the-art large language models (LLMs), including ChatGPT, Llama, Aya, Jais, and ACEGPT, in the task of Arabic automated essay scoring (AES) using the AR-AES dataset. It explores various evaluation methodologies, including zero-shot, few-shot in-context learning, and f... | {
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} |
2501.16519 | Optimizing Decentralized Online Learning for Supervised Regression and
Classification Problems | [
"cs.LG",
"cs.DC",
"cs.MA"
] | Decentralized learning networks aim to synthesize a single network inference from a set of raw inferences provided by multiple participants. To determine the combined inference, these networks must adopt a mapping from historical participant performance to weights, and to appropriately incentivize contributions they mu... | {
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} |
2501.16520 | Safe Gradient Flow for Bilevel Optimization | [
"math.OC",
"cs.LG",
"cs.SY",
"eess.SY"
] | Bilevel optimization is a key framework in hierarchical decision-making, where one problem is embedded within the constraints of another. In this work, we propose a control-theoretic approach to solving bilevel optimization problems. Our method consists of two components: a gradient flow mechanism to minimize the upper... | {
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} |
2501.16524 | Programming by Examples Meets Historical Linguistics: A Large Language
Model Based Approach to Sound Law Induction | [
"cs.CL"
] | Historical linguists have long written "programs" that convert reconstructed words in an ancestor language into their attested descendants via ordered string rewrite functions (called sound laws) However, writing these programs is time-consuming, motivating the development of automated Sound Law Induction (SLI) which w... | {
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} |
2501.16525 | Multi-Objective Deep-Learning-based Biomechanical Deformable Image
Registration with MOREA | [
"cs.CV",
"cs.AI",
"cs.NE"
] | When choosing a deformable image registration (DIR) approach for images with large deformations and content mismatch, the realism of found transformations often needs to be traded off against the required runtime. DIR approaches using deep learning (DL) techniques have shown remarkable promise in instantly predicting a... | {
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} |
2501.16533 | A comparison of data filtering techniques for English-Polish LLM-based
machine translation in the biomedical domain | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have become state-of-the-art in Machine Translation (MT), often trained on massive bilingual parallel corpora scraped from the web, that contain low-quality entries and redundant information, leading to significant computational challenges. Various data filtering methods exist to reduce dat... | {
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} |
2501.16534 | Targeting Alignment: Extracting Safety Classifiers of Aligned LLMs | [
"cs.CR",
"cs.AI"
] | Alignment in large language models (LLMs) is used to enforce guidelines such as safety. Yet, alignment fails in the face of jailbreak attacks that modify inputs to induce unsafe outputs. In this paper, we present and evaluate a method to assess the robustness of LLM alignment. We observe that alignment embeds a safety ... | {
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} |
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