id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.14379 | ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte
(TIL) assessment in breast cancer: Multicentre validation in 2,340 patients
with breast cancer | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with (triple-negative) breast cancer (BC). Computational TIL assessment (CTA) has the potential to assist pathologists in this labour-intensive task, but current CTA models rely heavily on many detailed annotations. We propose and v... | {
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2501.14390 | Distinguishing Parkinson's Patients Using Voice-Based Feature Extraction
and Classification | [
"cs.LG"
] | Parkinson's disease (PD) is a progressive neurodegenerative disorder that impacts motor functions and speech characteristics This study focuses on differentiating individuals with Parkinson's disease from healthy controls through the extraction and classification of speech features. Patients were further divided into 2... | {
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2501.14394 | Reinforcement Learning for Efficient Returns Management | [
"cs.LG"
] | In retail warehouses, returned products are typically placed in an intermediate storage until a decision regarding further shipment to stores is made. The longer products are held in storage, the higher the inefficiency and costs of the returns management process, since enough storage area has to be provided and mainta... | {
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2501.14399 | Handling Heterophily in Recommender Systems with Wavelet Hypergraph
Diffusion | [
"cs.IR",
"cs.AI",
"cs.DB",
"cs.LG",
"cs.SI"
] | Recommender systems are pivotal in delivering personalised user experiences across various domains. However, capturing the heterophily patterns and the multi-dimensional nature of user-item interactions poses significant challenges. To address this, we introduce FWHDNN (Fusion-based Wavelet Hypergraph Diffusion Neural ... | {
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2501.14400 | SKIL: Semantic Keypoint Imitation Learning for Generalizable
Data-efficient Manipulation | [
"cs.RO",
"cs.AI"
] | Real-world tasks such as garment manipulation and table rearrangement demand robots to perform generalizable, highly precise, and long-horizon actions. Although imitation learning has proven to be an effective approach for teaching robots new skills, large amounts of expert demonstration data are still indispensible fo... | {
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2501.14401 | CVOCSemRPL: Class-Variance Optimized Clustering, Semantic Information
Injection and Restricted Pseudo Labeling based Improved Semi-Supervised
Few-Shot Learning | [
"cs.CV"
] | Few-shot learning has been extensively explored to address problems where the amount of labeled samples is very limited for some classes. In the semi-supervised few-shot learning setting, substantial quantities of unlabeled samples are available. Such unlabeled samples are generally cheaper to obtain and can be used to... | {
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2501.14404 | Kolmogorov Arnold Neural Interpolator for Downscaling and Correcting
Meteorological Fields from In-Situ Observations | [
"cs.CV"
] | Obtaining accurate weather forecasts at station locations is a critical challenge due to systematic biases arising from the mismatch between multi-scale, continuous atmospheric characteristic and their discrete, gridded representations. Previous works have primarily focused on modeling gridded meteorological data, inhe... | {
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2501.14406 | Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning
of Language Models | [
"cs.DC",
"cs.AI",
"cs.LG",
"cs.NI"
] | Pre-trained Language Models (PLMs) have demonstrated their superiority and versatility in modern Natural Language Processing (NLP), effectively adapting to various downstream tasks through further fine-tuning. Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising solution to address privacy and... | {
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2501.14413 | Context-CrackNet: A Context-Aware Framework for Precise Segmentation of
Tiny Cracks in Pavement images | [
"cs.CV"
] | The accurate detection and segmentation of pavement distresses, particularly tiny and small cracks, are critical for early intervention and preventive maintenance in transportation infrastructure. Traditional manual inspection methods are labor-intensive and inconsistent, while existing deep learning models struggle wi... | {
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2501.14414 | SoK: What Makes Private Learning Unfair? | [
"cs.LG",
"cs.CR"
] | Differential privacy has emerged as the most studied framework for privacy-preserving machine learning. However, recent studies show that enforcing differential privacy guarantees can not only significantly degrade the utility of the model, but also amplify existing disparities in its predictive performance across demo... | {
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2501.14426 | CENTS: Generating synthetic electricity consumption time series for rare
and unseen scenarios | [
"cs.LG"
] | Recent breakthroughs in large-scale generative modeling have demonstrated the potential of foundation models in domains such as natural language, computer vision, and protein structure prediction. However, their application in the energy and smart grid sector remains limited due to the scarcity and heterogeneity of hig... | {
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2501.14427 | GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand
Graphs Better | [
"cs.LG"
] | The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text. However, unlike graph data, natural language inherently has sequential order. We observe a counter-intuitive fact that when the order of nodes o... | {
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2501.14430 | Statistical Verification of Linear Classifiers | [
"stat.ML",
"cs.LG",
"math.PR",
"math.ST",
"stat.AP",
"stat.TH"
] | We propose a homogeneity test closely related to the concept of linear separability between two samples. Using the test one can answer the question whether a linear classifier is merely ``random'' or effectively captures differences between two classes. We focus on establishing upper bounds for the test's \emph{p}-valu... | {
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2501.14431 | Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes
Domains | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically generate brief answers without reasoning processes and explanations. This limits users' confidence in making decisions based on their responses.... | {
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2501.14432 | CAMEO: Autocorrelation-Preserving Line Simplification for Lossy Time
Series Compression | [
"cs.DB",
"cs.IR",
"cs.IT",
"math.IT"
] | Time series data from a variety of sensors and IoT devices need effective compression to reduce storage and I/O bandwidth requirements. While most time series databases and systems rely on lossless compression, lossy techniques offer even greater space-saving with a small loss in precision. However, the unknown impact ... | {
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2501.14434 | Remining Hard Negatives for Generative Pseudo Labeled Domain Adaptation | [
"cs.IR",
"cs.LG"
] | Dense retrievers have demonstrated significant potential for neural information retrieval; however, they exhibit a lack of robustness to domain shifts, thereby limiting their efficacy in zero-shot settings across diverse domains. A state-of-the-art domain adaptation technique is Generative Pseudo Labeling (GPL). GPL us... | {
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2501.14438 | Data-efficient Performance Modeling via Pre-training | [
"cs.PL",
"cs.DC",
"cs.LG"
] | Performance models are essential for automatic code optimization, enabling compilers to predict the effects of code transformations on performance and guide search for optimal transformations. Building state-of-the-art performance models with deep learning, however, requires vast labeled datasets of random programs -- ... | {
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2501.14439 | Optimizing Human Pose Estimation Through Focused Human and Joint Regions | [
"cs.CV"
] | Human pose estimation has given rise to a broad spectrum of novel and compelling applications, including action recognition, sports analysis, as well as surveillance. However, accurate video pose estimation remains an open challenge. One aspect that has been overlooked so far is that existing methods learn motion clues... | {
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2501.14440 | Convergence of gradient based training for linear Graph Neural Networks | [
"cs.LG",
"cs.DM",
"cs.NA",
"cs.SI",
"math.NA"
] | Graph Neural Networks (GNNs) are powerful tools for addressing learning problems on graph structures, with a wide range of applications in molecular biology and social networks. However, the theoretical foundations underlying their empirical performance are not well understood. In this article, we examine the convergen... | {
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2501.14441 | Impact of Batch Normalization on Convolutional Network Representations | [
"cs.LG"
] | Batch normalization (BatchNorm) is a popular layer normalization technique used when training deep neural networks. It has been shown to enhance the training speed and accuracy of deep learning models. However, the mechanics by which BatchNorm achieves these benefits is an active area of research, and different perspec... | {
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2501.14442 | New scenarios and trends in non-traditional laboratories from 2000 to
2020 | [
"cs.CY",
"cs.SY",
"eess.SY"
] | For educational institutions in STEM areas, the provision of practical learning scenarios is, traditionally, a major concern. In the 21st century, the explosion of ICTs, as well as the universalization of low-cost hardware, have allowed the proliferation of technical solutions for any field; in the case of experimentat... | {
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2501.14443 | Learning more with the same effort: how randomization improves the
robustness of a robotic deep reinforcement learning agent | [
"cs.RO",
"cs.AI"
] | The industrial application of Deep Reinforcement Learning (DRL) is frequently slowed down because of the inability to generate the experience required to train the models. Collecting data often involves considerable time and economic effort that is unaffordable in most cases. Fortunately, devices like robots can be tra... | {
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2501.14451 | MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to
Detect Safety Violation in Autonomous Driving Systems | [
"cs.SE",
"cs.RO"
] | Autonomous Driving Systems (ADSs) are safety-critical, as real-world safety violations can result in significant losses. Rigorous testing is essential before deployment, with simulation testing playing a key role. However, ADSs are typically complex, consisting of multiple modules such as perception and planning, or we... | {
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2501.14452 | On the Rate-Exponent Region of Integrated Sensing and Communications
With Variable-Length Coding | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper considers the achievable rate-exponent region of integrated sensing and communication systems in the presence of variable-length coding with feedback. This scheme is fundamentally different from earlier studies, as the coding methods that utilize feedback impose different constraints on the codewords. The fo... | {
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2501.14453 | Optimal Strategies for Federated Learning Maintaining Client Privacy | [
"cs.LG"
] | Federated Learning (FL) emerged as a learning method to enable the server to train models over data distributed among various clients. These clients are protective about their data being leaked to the server, any other client, or an external adversary, and hence, locally train the model and share it with the server rat... | {
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2501.14455 | Triple Path Enhanced Neural Architecture Search for Multimodal Fake News
Detection | [
"cs.CV"
] | Multimodal fake news detection has become one of the most crucial issues on social media platforms. Although existing methods have achieved advanced performance, two main challenges persist: (1) Under-performed multimodal news information fusion due to model architecture solidification, and (2) weak generalization abil... | {
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2501.14457 | Understanding and Mitigating Gender Bias in LLMs via Interpretable
Neuron Editing | [
"cs.CL"
] | Large language models (LLMs) often exhibit gender bias, posing challenges for their safe deployment. Existing methods to mitigate bias lack a comprehensive understanding of its mechanisms or compromise the model's core capabilities. To address these issues, we propose the CommonWords dataset, to systematically evaluate... | {
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2501.14458 | A Survey of Optimization Methods for Training DL Models: Theoretical
Perspective on Convergence and Generalization | [
"cs.LG",
"cs.DC",
"math.OC"
] | As data sets grow in size and complexity, it is becoming more difficult to pull useful features from them using hand-crafted feature extractors. For this reason, deep learning (DL) frameworks are now widely popular. The Holy Grail of DL and one of the most mysterious challenges in all of modern ML is to develop a funda... | {
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2501.14459 | Interpretability Analysis of Domain Adapted Dense Retrievers | [
"cs.IR",
"cs.AI"
] | Dense retrievers have demonstrated significant potential for neural information retrieval; however, they exhibit a lack of robustness to domain shifts, thereby limiting their efficacy in zero-shot settings across diverse domains. Previous research has investigated unsupervised domain adaptation techniques to adapt dens... | {
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2501.14460 | MLMC: Interactive multi-label multi-classifier evaluation without
confusion matrices | [
"cs.LG"
] | Machine learning-based classifiers are commonly evaluated by metrics like accuracy, but deeper analysis is required to understand their strengths and weaknesses. MLMC is a visual exploration tool that tackles the challenge of multi-label classifier comparison and evaluation. It offers a scalable alternative to confusio... | {
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2501.14466 | On Correlating Factors for Domain Adaptation Performance | [
"cs.IR",
"stat.AP"
] | Dense retrievers have demonstrated significant potential for neural information retrieval; however, they lack robustness to domain shifts, limiting their efficacy in zero-shot settings across diverse domains. In this paper, we set out to analyze the possible factors that lead to successful domain adaptation of dense re... | {
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2501.14469 | Pesti-Gen: Unleashing a Generative Molecule Approach for Toxicity Aware
Pesticide Design | [
"cs.LG",
"cs.AI",
"q-bio.BM",
"q-bio.MN"
] | Global climate change has reduced crop resilience and pesticide efficacy, making reliance on synthetic pesticides inevitable, even though their widespread use poses significant health and environmental risks. While these pesticides remain a key tool in pest management, previous machine-learning applications in pesticid... | {
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2501.14473 | XFSC: A Catalogue of Trustable Semantic Metadata for Data Services and
Providers | [
"cs.DB"
] | In dataspaces, federation services facilitate key functions such as enabling participating organizations to establish mutual trust and assisting them in discovering data and services available for consumption. Discovery is enabled by a catalogue, where participants publish metadata describing themselves and their data ... | {
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2501.14474 | The Pseudo-Dimension of Contracts | [
"cs.GT",
"cs.AI",
"cs.LG",
"econ.TH"
] | Algorithmic contract design studies scenarios where a principal incentivizes an agent to exert effort on her behalf. In this work, we focus on settings where the agent's type is drawn from an unknown distribution, and formalize an offline learning framework for learning near-optimal contracts from sample agent types. A... | {
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2501.14476 | Avoiding Overfitting in Variable-Order Markov Models: a Cross-Validation
Approach | [
"physics.soc-ph",
"cs.SI",
"econ.GN",
"q-fin.EC"
] | Higher$\text{-}$order Markov chain models are widely used to represent agent transitions in dynamic systems, such as passengers in transport networks. They capture transitions in complex systems by considering not only the current state but also the path of previously visited states. For example, the likelihood of trai... | {
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2501.14483 | Registration of Longitudinal Liver Examinations for Tumor Progress
Assessment | [
"eess.IV",
"cs.AI",
"cs.CV",
"physics.med-ph"
] | Assessing cancer progression in liver CT scans is a clinical challenge, requiring a comparison of scans at different times for the same patient. Practitioners must identify existing tumors, compare them with prior exams, identify new tumors, and evaluate overall disease evolution. This process is particularly complex i... | {
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2501.14484 | $SpikePack$: Enhanced Information Flow in Spiking Neural Networks with
High Hardware Compatibility | [
"cs.NE"
] | Spiking Neural Networks (SNNs) hold promise for energy-efficient, biologically inspired computing. We identify substantial informatio loss during spike transmission, linked to temporal dependencies in traditional Leaky Integrate-and-Fire (LIF) neuron-a key factor potentially limiting SNN performance. Existing SNN archi... | {
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2501.14486 | Visual-Lidar Map Alignment for Infrastructure Inspections | [
"cs.RO"
] | Routine and repetitive infrastructure inspections present safety, efficiency, and consistency challenges as they are performed manually, often in challenging or hazardous environments. They can also introduce subjectivity and errors into the process, resulting in undesirable outcomes. Simultaneous localization and mapp... | {
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2501.14488 | Breaking the Pre-Planning Barrier: Real-Time Adaptive Coordination of
Mission and Charging UAVs Using Graph Reinforcement Learning | [
"cs.MA"
] | Unmanned Aerial Vehicles (UAVs) are pivotal in applications such as search and rescue and environmental monitoring, excelling in intelligent perception tasks. However, their limited battery capacity hinders long-duration and long-distance missions. Charging UAVs (CUAVs) offers a potential solution by recharging mission... | {
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2501.14490 | Channel-wise Parallelizable Spiking Neuron with Multiplication-free
Dynamics and Large Temporal Receptive Fields | [
"cs.NE"
] | Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their sophisticated neuronal dynamics and sparse binary activations (spikes) inspired by the biological neural system. Traditional neuron models use iterative step-by-step dynamics, resulting in serial computation and slow train... | {
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2501.14491 | Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer:
Tasks and Experimental Setups Matter | [
"cs.CL"
] | Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include is unclear. Prior research often focuses on a small set of languages from a few language families and/or a single task. I... | {
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2501.14492 | RealCritic: Towards Effectiveness-Driven Evaluation of Language Model
Critiques | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Critiques are important for enhancing the performance of Large Language Models (LLMs), enabling both self-improvement and constructive feedback for others by identifying flaws and suggesting improvements. However, evaluating the critique capabilities of LLMs presents a significant challenge due to the open-ended nature... | {
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2501.14495 | BILLNET: A Binarized Conv3D-LSTM Network with Logic-gated residual
architecture for hardware-efficient video inference | [
"cs.CV",
"cs.AR"
] | Long Short-Term Memory (LSTM) and 3D convolution (Conv3D) show impressive results for many video-based applications but require large memory and intensive computing. Motivated by recent works on hardware-algorithmic co-design towards efficient inference, we propose a compact binarized Conv3D-LSTM model architecture cal... | {
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2501.14496 | A Note on Implementation Errors in Recent Adaptive Attacks Against
Multi-Resolution Self-Ensembles | [
"cs.CR",
"cs.CV",
"cs.LG"
] | This note documents an implementation issue in recent adaptive attacks (Zhang et al. [2024]) against the multi-resolution self-ensemble defense (Fort and Lakshminarayanan [2024]). The implementation allowed adversarial perturbations to exceed the standard $L_\infty = 8/255$ bound by up to a factor of 20$\times$, reachi... | {
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2501.14497 | Evaluating and Improving Graph to Text Generation with Large Language
Models | [
"cs.CL"
] | Large language models (LLMs) have demonstrated immense potential across various tasks. However, research for exploring and improving the capabilities of LLMs in interpreting graph structures remains limited. To address this gap, we conduct a comprehensive evaluation of prompting current open-source LLMs on graph-to-tex... | {
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2501.14499 | Automated Assignment Grading with Large Language Models: Insights From a
Bioinformatics Course | [
"cs.LG",
"cs.CY"
] | Providing students with individualized feedback through assignments is a cornerstone of education that supports their learning and development. Studies have shown that timely, high-quality feedback plays a critical role in improving learning outcomes. However, providing personalized feedback on a large scale in classes... | {
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2501.14502 | LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing | [
"cs.RO",
"cs.CV"
] | Autonomous racing provides a controlled environment for testing the software and hardware of autonomous vehicles operating at their performance limits. Competitive interactions between multiple autonomous racecars however introduce challenging and potentially dangerous scenarios. Accurate and consistent vehicle detecti... | {
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2501.14503 | Benchmarking global optimization techniques for unmanned aerial vehicle
path planning | [
"cs.NE",
"cs.RO",
"math.OC"
] | The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we co... | {
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2501.14506 | WanJuanSiLu: A High-Quality Open-Source Webtext Dataset for Low-Resource
Languages | [
"cs.CL"
] | This paper introduces the open-source dataset WanJuanSiLu, designed to provide high-quality training corpora for low-resource languages, thereby advancing the research and development of multilingual models. To achieve this, we have developed a systematic data processing framework tailored for low-resource languages. T... | {
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2501.14510 | Deep-BrownConrady: Prediction of Camera Calibration and Distortion
Parameters Using Deep Learning and Synthetic Data | [
"cs.CV",
"cs.LG"
] | This research addresses the challenge of camera calibration and distortion parameter prediction from a single image using deep learning models. The main contributions of this work are: (1) demonstrating that a deep learning model, trained on a mix of real and synthetic images, can accurately predict camera and lens par... | {
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2501.14513 | ABPT: Amended Backpropagation through Time with Partially Differentiable
Rewards | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Using the exact gradients of the rewards to directly optimize policy parameters via backpropagation-through-time (BPTT) enables high training performance for quadrotor tasks. However, designing a fully differentiable reward architecture is often challenging. Partially differentiable rewards will result in biased gradie... | {
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2501.14514 | PARASIDE: An Automatic Paranasal Sinus Segmentation and Structure
Analysis Tool for MRI | [
"cs.CV",
"cs.LG"
] | Chronic rhinosinusitis (CRS) is a common and persistent sinus imflammation that affects 5 - 12\% of the general population. It significantly impacts quality of life and is often difficult to assess due to its subjective nature in clinical evaluation. We introduce PARASIDE, an automatic tool for segmenting air and soft ... | {
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2501.14520 | Scene Understanding Enabled Semantic Communication with Open Channel
Coding | [
"eess.SP",
"cs.CV"
] | As communication systems transition from symbol transmission to conveying meaningful information, sixth-generation (6G) networks emphasize semantic communication. This approach prioritizes high-level semantic information, improving robustness and reducing redundancy across modalities like text, speech, and images. Howe... | {
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2501.14522 | Information Age and Correctness for Energy Harvesting Devices with
Random Access | [
"cs.IT",
"math.IT"
] | We study a large network of energy-harvesting devices that monitor two-state Markov processes and send status updates to a gateway using the slotted ALOHA protocol without feedback. We let the devices adjust their transmission probabilities according to their process state transitions and current battery levels. Using ... | {
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2501.14524 | Training-Free Style and Content Transfer by Leveraging U-Net Skip
Connections in Stable Diffusion 2.* | [
"cs.CV"
] | Despite significant recent advances in image generation with diffusion models, their internal latent representations remain poorly understood. Existing works focus on the bottleneck layer (h-space) of Stable Diffusion's U-Net or leverage the cross-attention, self-attention, or decoding layers. Our model, SkipInject tak... | {
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2501.14526 | Robustified Time-optimal Point-to-point Motion Planning and Control
under Uncertainty | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper proposes a novel approach to formulate time-optimal point-to-point motion planning and control under uncertainty. The approach defines a robustified two-stage Optimal Control Problem (OCP), in which stage 1, with a fixed time grid, is seamlessly stitched with stage 2, which features a variable time grid. Sta... | {
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2501.14528 | Idiom Detection in Sorani Kurdish Texts | [
"cs.CL"
] | Idiom detection using Natural Language Processing (NLP) is the computerized process of recognizing figurative expressions within a text that convey meanings beyond the literal interpretation of the words. While idiom detection has seen significant progress across various languages, the Kurdish language faces a consider... | {
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2501.14531 | On Hardening DNNs against Noisy Computations | [
"cs.LG"
] | The success of deep learning has sparked significant interest in designing computer hardware optimized for the high computational demands of neural network inference. As further miniaturization of digital CMOS processors becomes increasingly challenging, alternative computing paradigms, such as analog computing, are ga... | {
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2501.14533 | CheapNVS: Real-Time On-Device Narrow-Baseline Novel View Synthesis | [
"cs.CV"
] | Single-view novel view synthesis (NVS) is a notorious problem due to its ill-posed nature, and often requires large, computationally expensive approaches to produce tangible results. In this paper, we propose CheapNVS: a fully end-to-end approach for narrow baseline single-view NVS based on a novel, efficient multiple ... | {
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2501.14534 | Trick-GS: A Balanced Bag of Tricks for Efficient Gaussian Splatting | [
"cs.CV"
] | Gaussian splatting (GS) for 3D reconstruction has become quite popular due to their fast training, inference speeds and high quality reconstruction. However, GS-based reconstructions generally consist of millions of Gaussians, which makes them hard to use on computationally constrained devices such as smartphones. In t... | {
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2501.14535 | Rethinking Encoder-Decoder Flow Through Shared Structures | [
"cs.CV",
"cs.LG"
] | Dense prediction tasks have enjoyed a growing complexity of encoder architectures, decoders, however, have remained largely the same. They rely on individual blocks decoding intermediate feature maps sequentially. We introduce banks, shared structures that are used by each decoding block to provide additional context i... | {
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2501.14539 | A Recurrent Spiking Network with Hierarchical Intrinsic Excitability
Modulation for Schema Learning | [
"cs.NE",
"cs.LG"
] | Schema, a form of structured knowledge that promotes transfer learning, is attracting growing attention in both neuroscience and artificial intelligence (AI). Current schema research in neural computation is largely constrained to a single behavioral paradigm and relies heavily on recurrent neural networks (RNNs) which... | {
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2501.14540 | VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic
Reasoning | [
"cs.AI"
] | A recent approach to neurosymbolic reasoning is to explicitly combine the strengths of large language models (LLMs) and symbolic solvers to tackle complex reasoning tasks. However, current approaches face significant limitations, including poor generalizability due to task-specific prompts, inefficiencies caused by the... | {
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2501.14543 | Reducing Action Space for Deep Reinforcement Learning via Causal Effect
Estimation | [
"cs.LG"
] | Intelligent decision-making within large and redundant action spaces remains challenging in deep reinforcement learning. Considering similar but ineffective actions at each step can lead to repetitive and unproductive trials. Existing methods attempt to improve agent exploration by reducing or penalizing redundant acti... | {
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2501.14544 | Distributed Conformal Prediction via Message Passing | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Post-hoc calibration of pre-trained models is critical for ensuring reliable inference, especially in safety-critical domains such as healthcare. Conformal Prediction (CP) offers a robust post-hoc calibration framework, providing distribution-free statistical coverage guarantees for prediction sets by leveraging held-o... | {
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2501.14546 | Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite
Images by Poverty Level: Advancing Tools for Social Science Research | [
"cs.CV",
"cs.AI"
] | This paper investigates the novel application of Large Language Models (LLMs) with vision capabilities to analyze satellite imagery for village-level poverty prediction. Although LLMs were originally designed for natural language understanding, their adaptability to multimodal tasks, including geospatial analysis, has ... | {
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2501.14548 | Large-scale and Fine-grained Vision-language Pre-training for Enhanced
CT Image Understanding | [
"cs.CV"
] | Artificial intelligence (AI) shows great potential in assisting radiologists to improve the efficiency and accuracy of medical image interpretation and diagnosis. However, a versatile AI model requires large-scale data and comprehensive annotations, which are often impractical in medical settings. Recent studies levera... | {
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2501.14551 | Fairness of Deep Ensembles: On the interplay between per-group task
difficulty and under-representation | [
"cs.LG"
] | Ensembling is commonly regarded as an effective way to improve the general performance of models in machine learning, while also increasing the robustness of predictions. When it comes to algorithmic fairness, heterogeneous ensembles, composed of multiple model types, have been employed to mitigate biases in terms of d... | {
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2501.14557 | Optimizing Grasping Precision for Industrial Pick-and-Place Tasks
Through a Novel Visual Servoing Approach | [
"cs.RO"
] | The integration of robotic arm manipulators into industrial manufacturing lines has become common, thanks to their efficiency and effectiveness in executing specific tasks. With advancements in camera technology, visual sensors and perception systems have been incorporated to address more complex operations. This study... | {
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2501.14568 | Hybrid Quantum-Classical Multi-Agent Pathfinding | [
"cs.AI",
"quant-ph"
] | Multi-Agent Path Finding (MAPF) focuses on determining conflict-free paths for multiple agents navigating through a shared space to reach specified goal locations. This problem becomes computationally challenging, particularly when handling large numbers of agents, as frequently encountered in practical applications li... | {
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2501.14570 | coverforest: Conformal Predictions with Random Forest in Python | [
"stat.ML",
"cs.LG",
"stat.CO"
] | Conformal prediction provides a framework for uncertainty quantification, specifically in the forms of prediction intervals and sets with distribution-free guaranteed coverage. While recent cross-conformal techniques such as CV+ and Jackknife+-after-bootstrap achieve better data efficiency than traditional split confor... | {
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2501.14573 | A Transferable Physics-Informed Framework for Battery Degradation
Diagnosis, Knee-Onset Detection and Knee Prediction | [
"eess.SY",
"cs.SY"
] | The techno-economic and safety concerns of battery capacity knee occurrence call for developing online knee detection and prediction methods as an advanced battery management system (BMS) function. To address this, a transferable physics-informed framework that consists of a histogram-based feature engineering method, ... | {
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2501.14576 | Dynamic Operation and Control of a Multi-Stack Alkaline Water
Electrolysis System with Shared Gas Separators and Lye Circulation: A
Model-Based Study | [
"math.OC",
"cs.SY",
"eess.SY"
] | An emerging approach for large-scale hydrogen production using renewable energy is to integrate multiple alkaline water electrolysis (AWE) stacks into a single balance of plant (BoP) system, sharing components such as gas-lye separation and lye circulation. This configuration, termed the $N$-in-1 AWE system, packs $N$ ... | {
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2501.14577 | ZETA: Leveraging Z-order Curves for Efficient Top-k Attention | [
"cs.LG",
"cs.AI"
] | Over recent years, the Transformer has become a fundamental building block for sequence modeling architectures. Yet at its core is the use of self-attention, whose memory and computational cost grow quadratically with the sequence length $N$, rendering it prohibitively expensive for long sequences. A promising approach... | {
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2501.14579 | Knowledge Graphs Construction from Criminal Court Appeals: Insights from
the French Cassation Court | [
"cs.IR"
] | Despite growing interest, accurately and reliably representing unstructured data, such as court decisions, in a structured form, remains a challenge. Recent advancements in generative AI applied to language modeling enabled the transformation of text into knowledge graphs, unlocking new opportunities for analysis and m... | {
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2501.14586 | A sub-structuring approach for model reduction of frictionally clamped
thin-walled structures | [
"eess.SY",
"cs.SY"
] | Thin-walled structures clamped by friction joints, such as aircraft skin panels are exposed to bending-stretching coupling and frictional contact. We propose an original sub-structuring approach, where the system is divided into thin-walled and support regions, so that geometrically nonlinear behavior is relevant only ... | {
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2501.14587 | Visual Localization via Semantic Structures in Autonomous Photovoltaic
Power Plant Inspection | [
"cs.CV",
"cs.RO"
] | Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a challenging problem as it requires precise navigation to capture images from optimal distances and vi... | {
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2501.14588 | Data Assetization via Resources-decoupled Federated Learning | [
"cs.LG"
] | With the development of the digital economy, data is increasingly recognized as an essential resource for both work and life. However, due to privacy concerns, data owners tend to maximize the value of data through the circulation of information rather than direct data transfer. Federated learning (FL) provides an effe... | {
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2501.14592 | Improved Vessel Segmentation with Symmetric Rotation-Equivariant U-Net | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Automated segmentation plays a pivotal role in medical image analysis and computer-assisted interventions. Despite the promising performance of existing methods based on convolutional neural networks (CNNs), they neglect useful equivariant properties for images, such as rotational and reflection equivariance. This limi... | {
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2501.14593 | Geometric Mean Improves Loss For Few-Shot Learning | [
"cs.CV"
] | Few-shot learning (FSL) is a challenging task in machine learning, demanding a model to render discriminative classification by using only a few labeled samples. In the literature of FSL, deep models are trained in a manner of metric learning to provide metric in a feature space which is well generalizable to classify ... | {
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2501.14600 | On the Homophily of Heterogeneous Graphs: Understanding and Unleashing | [
"cs.SI"
] | Homophily, the tendency of similar nodes to connect, is a fundamental phenomenon in network science and a critical factor in the performance of graph neural networks (GNNs). While existing studies primarily explore homophily in homogeneous graphs, where nodes share the same type, real-world networks are often more accu... | {
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2501.14603 | Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV
Networks | [
"cs.LG",
"cs.AI"
] | Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is ... | {
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2501.14604 | Inverse Evolution Data Augmentation for Neural PDE Solvers | [
"cs.LG"
] | Neural networks have emerged as promising tools for solving partial differential equations (PDEs), particularly through the application of neural operators. Training neural operators typically requires a large amount of training data to ensure accuracy and generalization. In this paper, we propose a novel data augmenta... | {
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2501.14605 | 3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic
Segmentation in Autonomous Driving | [
"cs.CV"
] | Domain generalization aims to find ways for deep learning models to maintain their performance despite significant domain shifts between training and inference datasets. This is particularly important for models that need to be robust or are costly to train. LiDAR perception in autonomous driving is impacted by both of... | {
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2501.14607 | ReferDINO: Referring Video Object Segmentation with Visual Grounding
Foundations | [
"cs.CV"
] | Referring video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description. Despite notable progress in recent years, current RVOS models remain struggle to handle complicated object descriptions due to their limited video-language understanding. To address this limitation,... | {
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2501.14610 | Leveraging Spatial Cues from Cochlear Implant Microphones to Efficiently
Enhance Speech Separation in Real-World Listening Scenes | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Speech separation approaches for single-channel, dry speech mixtures have significantly improved. However, real-world spatial and reverberant acoustic environments remain challenging, limiting the effectiveness of these approaches for assistive hearing devices like cochlear implants (CIs). To address this, we quantify ... | {
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2501.14615 | Single-neuron deep generative model uncovers underlying physics of
neuronal activity in Ca imaging data | [
"q-bio.NC",
"cs.LG"
] | Calcium imaging has become a powerful alternative to electrophysiology for studying neuronal activity, offering spatial resolution and the ability to measure large populations of neurons in a minimally invasive manner. This technique has broad applications in neuroscience, neuroengineering, and medicine, enabling resea... | {
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2501.14616 | QuIP: Experimental design for expensive simulators with many Qualitative
factors via Integer Programming | [
"stat.AP",
"cs.RO"
] | The need to explore and/or optimize expensive simulators with many qualitative factors arises in broad scientific and engineering problems. Our motivating application lies in path planning - the exploration of feasible paths for navigation, which plays an important role in robotics, surgical planning and assembly plann... | {
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2501.14617 | Funzac at CoMeDi Shared Task: Modeling Annotator Disagreement from
Word-In-Context Perspectives | [
"cs.CL"
] | In this work, we evaluate annotator disagreement in Word-in-Context (WiC) tasks exploring the relationship between contextual meaning and disagreement as part of the CoMeDi shared task competition. While prior studies have modeled disagreement by analyzing annotator attributes with single-sentence inputs, this shared t... | {
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2501.14620 | Strong Converse Exponent for Remote Lossy Source Coding | [
"cs.IT",
"math.IT"
] | Past works on remote lossy source coding studied the rate under average distortion and the error exponent of excess distortion probability. In this work, we look into how fast the excess distortion probability converges to 1 at small rates, also known as exponential strong converse. We characterize its exponent by esta... | {
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2501.14622 | ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy
Representation Learning | [
"cs.LG",
"cs.AI"
] | Learning efficient representations for decision-making policies is a challenge in imitation learning (IL). Current IL methods require expert demonstrations, which are expensive to collect. Consequently, they often have underdeveloped world models. Self-supervised learning (SSL) offers an alternative by allowing models ... | {
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} |
2501.14625 | Accelerated Preference Elicitation with LLM-Based Proxies | [
"cs.GT",
"cs.LG"
] | Bidders in combinatorial auctions face significant challenges when describing their preferences to an auctioneer. Classical work on preference elicitation focuses on query-based techniques inspired from proper learning--often via proxies that interface between bidders and an auction mechanism--to incrementally learn bi... | {
"Other": 1,
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} |
2501.14630 | Extracting Problem Structure with LLMs for Optimized SAT Local Search | [
"cs.AI"
] | Local search preprocessing makes Conflict-Driven Clause Learning (CDCL) solvers faster by providing high-quality starting points and modern SAT solvers have incorporated this technique into their preprocessing steps. However, these tools rely on basic strategies that miss the structural patterns in problems. We present... | {
"Other": 0,
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} |
2501.14633 | Channel Independent Precoder for OFDM-based Systems over Fading Channels | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this paper we propose an independent channel precoder for orthogonal frequency division multiplexing (OFDM) systems over fading channels. The design of the precoder is based on the information redistribution of the input modulated symbols amongst the output precoded symbols. The proposed precoder decreases the varia... | {
"Other": 0,
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} |
2501.14634 | Recommending Actionable Strategies: A Semantic Approach to Integrating
Analytical Frameworks with Decision Heuristics | [
"cs.AI"
] | We present a novel approach for recommending actionable strategies by integrating strategic frameworks with decision heuristics through semantic analysis. While strategy frameworks provide systematic models for assessment and planning, and decision heuristics encode experiential knowledge,these traditions have historic... | {
"Other": 0,
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} |
2501.14635 | Optimal Transport Barycenter via Nonconvex-Concave Minimax Optimization | [
"stat.ML",
"cs.LG"
] | The optimal transport barycenter (a.k.a. Wasserstein barycenter) is a fundamental notion of averaging that extends from the Euclidean space to the Wasserstein space of probability distributions. Computation of the unregularized barycenter for discretized probability distributions on point clouds is a challenging task w... | {
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.14636 | A Paired Autoencoder Framework for Inverse Problems via Bayes Risk
Minimization | [
"cs.LG",
"cs.NA",
"math.NA"
] | In this work, we describe a new data-driven approach for inverse problems that exploits technologies from machine learning, in particular autoencoder network structures. We consider a paired autoencoder framework, where two autoencoders are used to efficiently represent the input and target spaces separately and optima... | {
"Other": 1,
"cs.AI": 0,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.14637 | The Paradox of Intervention: Resilience in Adaptive Multi-Role
Coordination Networks | [
"physics.soc-ph",
"cs.SI"
] | Complex adaptive networks exhibit remarkable resilience, driven by the dynamic interplay of structure (interactions) and function (state). While static-network analyses offer valuable insights, understanding how structure and function co-evolve under external interventions is critical for explaining system-level adapta... | {
"Other": 0,
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"cs.SI": 1,
"cs.SY": 0
} |
2501.14641 | Towards Scalable Topological Regularizers | [
"cs.LG",
"math.AT"
] | Latent space matching, which consists of matching distributions of features in latent space, is a crucial component for tasks such as adversarial attacks and defenses, domain adaptation, and generative modelling. Metrics for probability measures, such as Wasserstein and maximum mean discrepancy, are commonly used to qu... | {
"Other": 0,
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"cs.MA": 0,
"cs.NE": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.14644 | Whisper D-SGD: Correlated Noise Across Agents for Differentially Private
Decentralized Learning | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.DC"
] | Decentralized learning enables distributed agents to train a shared machine learning model through local computation and peer-to-peer communication. Although each agent retains its dataset locally, the communication of local models can still expose private information to adversaries. To mitigate these threats, local di... | {
"Other": 1,
"cs.AI": 1,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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