id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.10642 | Iterative Tree Analysis for Medical Critics | [
"cs.CL"
] | Large Language Models (LLMs) have been widely adopted across various domains, yet their application in the medical field poses unique challenges, particularly concerning the generation of hallucinations. Hallucinations in open-ended long medical text manifest as misleading critical claims, which are difficult to verify... | {
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2501.10644 | UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing | [
"cs.LG",
"cs.MA"
] | The rapid development of Unmanned aerial vehicles (UAVs) technology has spawned a wide variety of applications, such as emergency communications, regional surveillance, and disaster relief. Due to their limited battery capacity and processing power, multiple UAVs are often required for complex tasks. In such cases, a c... | {
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2501.10645 | Constrained Coding for Composite DNA: Channel Capacity and Efficient
Constructions | [
"cs.IT",
"math.IT"
] | Composite DNA is a recent novel method to increase the information capacity of DNA-based data storage above the theoretical limit of 2 bits/symbol. In this method, every composite symbol does not store a single DNA nucleotide but a mixture of the four nucleotides in a predetermined ratio. By using different mixtures an... | {
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2501.10648 | DNA 1.0 Technical Report | [
"cs.CL"
] | In this report, we present DNA 1.0 8B Instruct, a state-of-the-art bilingual language model optimized for Korean and English language tasks. By applying continual pre-training (CPT) with high-quality Korean datasets to Llama 3.1 8B and subsequent supervised fine-tuning (SFT), we create an instruction-following model wi... | {
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2501.10651 | MOFA: Discovering Materials for Carbon Capture with a GenAI- and
Simulation-Based Workflow | [
"cs.DC",
"cond-mat.mtrl-sci",
"cs.LG"
] | We present MOFA, an open-source generative AI (GenAI) plus simulation workflow for high-throughput generation of metal-organic frameworks (MOFs) on large-scale high-performance computing (HPC) systems. MOFA addresses key challenges in integrating GPU-accelerated computing for GPU-intensive GenAI tasks, including distri... | {
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2501.10658 | LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning
Accelerator | [
"cs.AR",
"cs.AI",
"cs.LG"
] | The emergence of neural network capabilities invariably leads to a significant surge in computational demands due to expanding model sizes and increased computational complexity. To reduce model size and lower inference costs, recent research has focused on simplifying models and designing hardware accelerators using l... | {
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2501.10661 | Unveiling the Mystery of Weight in Large Foundation Models: Gaussian
Distribution Never Fades | [
"cs.LG",
"cs.AI",
"cs.CL"
] | This paper presents a pioneering exploration of the mechanisms underlying large foundation models' (LFMs) weights, aiming to simplify AI research. Through extensive observation and analysis on prevailing LFMs, we find that regardless of initialization strategies, their weights predominantly follow a Gaussian distributi... | {
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2501.10663 | PB-NBV: Efficient Projection-Based Next-Best-View Planning Framework for
Reconstruction of Unknown Objects | [
"cs.RO"
] | Completely capturing the three-dimensional (3D) data of an object is essential in industrial and robotic applications. The task of next-best-view (NBV) planning is to calculate the next optimal viewpoint based on the current data, gradually achieving a complete 3D reconstruction of the object. However, many existing NB... | {
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2501.10666 | Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture | [
"cs.SD",
"cs.LG",
"eess.AS"
] | Emotion detection techniques have been applied to multiple cases mainly from facial image features and vocal audio features, of which the latter aspect is disputed yet not only due to the complexity of speech audio processing but also the difficulties of extracting appropriate features. Part of the SAVEE and RAVDESS da... | {
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2501.10667 | Precision Adaptive Imputation Network : An Unified Technique for Mixed
Datasets | [
"cs.LG",
"stat.ML"
] | The challenge of missing data remains a significant obstacle across various scientific domains, necessitating the development of advanced imputation techniques that can effectively address complex missingness patterns. This study introduces the Precision Adaptive Imputation Network (PAIN), a novel algorithm designed to... | {
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2501.10668 | MappedTrace: Tracing Pointer Remotely with Compiler-generated Maps | [
"cs.PL",
"cs.CL"
] | Existing precise pointer tracing methods introduce substantial runtime overhead to the program being traced and are applicable only at specific program execution points. We propose MappedTrace that leverages compiler-generated read-only maps to accurately identify all pointers in any given snapshot of a program's execu... | {
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2501.10670 | Computing Capacity-Cost Functions for Continuous Channels in Wasserstein
Space | [
"cs.IT",
"eess.SP",
"math.IT",
"math.OC"
] | This paper investigates the problem of computing capacity-cost (C-C) functions for continuous channels. Motivated by the Kullback-Leibler divergence (KLD) proximal reformulation of the classical Blahut-Arimoto (BA) algorithm, the Wasserstein distance is introduced to the proximal term for the continuous case, resulting... | {
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2501.10672 | Homotopical Entropy | [
"math.CT",
"cs.IT",
"math-ph",
"math.IT",
"math.MP"
] | We present a "homotopification" of fundamental concepts from information theory. Using homotopy type theory, we define homotopy types that behave analogously to probability spaces, random variables, and the exponentials of Shannon entropy and relative entropy. The original analytic theories emerge through homotopy card... | {
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2501.10673 | Hybrid-Quantum Neural Architecture Search for The Proximal Policy
Optimization Algorithm | [
"quant-ph",
"cs.LG",
"cs.NE"
] | Recent studies in quantum machine learning advocated the use of hybrid models to assist with the limitations of the currently existing Noisy Intermediate Scale Quantum (NISQ) devices, but what was missing from most of them was the explanations and interpretations of the choices that were made to pick those exact archit... | {
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2501.10674 | Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The
answer is No! | [
"cs.CV",
"cs.CL"
] | Multimodal Large Language Models (MLLMs) have achieved significant advancements in tasks like Visual Question Answering (VQA) by leveraging foundational Large Language Models (LLMs). However, their abilities in specific areas such as visual temporal understanding, which is crucial for comprehending real-world dynamics,... | {
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2501.10677 | Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable
Pretrained Model on Credit Scoring | [
"cs.LG",
"cs.AI",
"q-fin.RM"
] | The advent of artificial intelligence has significantly enhanced credit scoring technologies. Despite the remarkable efficacy of advanced deep learning models, mainstream adoption continues to favor tree-structured models due to their robust predictive performance on tabular data. Although pretrained models have seen c... | {
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2501.10684 | Deep Operator Networks for Bayesian Parameter Estimation in PDEs | [
"cs.LG",
"cs.CE",
"stat.ML"
] | We present a novel framework combining Deep Operator Networks (DeepONets) with Physics-Informed Neural Networks (PINNs) to solve partial differential equations (PDEs) and estimate their unknown parameters. By integrating data-driven learning with physical constraints, our method achieves robust and accurate solutions a... | {
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2501.10685 | Harnessing the Potential of Large Language Models in Modern Marketing
Management: Applications, Future Directions, and Strategic Recommendations | [
"cs.CL"
] | Large Language Models (LLMs) have revolutionized the process of customer engagement, campaign optimization, and content generation, in marketing management. In this paper, we explore the transformative potential of LLMs along with the current applications, future directions, and strategic recommendations for marketers.... | {
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2501.10687 | EMO2: End-Effector Guided Audio-Driven Avatar Video Generation | [
"cs.CV"
] | In this paper, we propose a novel audio-driven talking head method capable of simultaneously generating highly expressive facial expressions and hand gestures. Unlike existing methods that focus on generating full-body or half-body poses, we investigate the challenges of co-speech gesture generation and identify the we... | {
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2501.10688 | Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers | [
"cs.LG",
"cs.AI",
"cs.CC",
"cs.CL"
] | Looped Transformers have shown exceptional neural algorithmic reasoning capability in simulating traditional graph algorithms, but their application to more complex structures like hypergraphs remains underexplored. Hypergraphs generalize graphs by modeling higher-order relationships among multiple entities, enabling r... | {
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2501.10690 | Insights from the application of nonlinear model predictive control to a
cart-pendulum | [
"eess.SY",
"cs.SY"
] | Inspired greatly by Mills et al. (2009) and the solution within, this paper aims to more clearly explain the mathematics and implementation details of such a powerful control algorithm. While the aforementioned paper is well written and of sound mathematics, it is extreamly dense and requires some time and patien... | {
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2501.10692 | Multi-modal Fusion and Query Refinement Network for Video Moment
Retrieval and Highlight Detection | [
"cs.CV"
] | Given a video and a linguistic query, video moment retrieval and highlight detection (MR&HD) aim to locate all the relevant spans while simultaneously predicting saliency scores. Most existing methods utilize RGB images as input, overlooking the inherent multi-modal visual signals like optical flow and depth. In this p... | {
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2501.10693 | Distributionally Robust Policy Evaluation and Learning for Continuous
Treatment with Observational Data | [
"cs.AI",
"cs.LG"
] | Using offline observational data for policy evaluation and learning allows decision-makers to evaluate and learn a policy that connects characteristics and interventions. Most existing literature has focused on either discrete treatment spaces or assumed no difference in the distributions between the policy-learning an... | {
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2501.10694 | Energy Efficiency Maximization for Movable Antenna-Enhanced System Based
on Statistical CSI | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper investigates an innovative movable antenna (MA)-enhanced multiple-input multiple-output (MIMO) system designed to enhance communication performance. We aim to maximize the energy efficiency (EE) under statistical channel state information (S-CSI) through a joint optimization of the transmit covariance matrix... | {
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2501.10695 | Exploring Transferable Homogeneous Groups for Compositional Zero-Shot
Learning | [
"cs.CV"
] | Conditional dependency present one of the trickiest problems in Compositional Zero-Shot Learning, leading to significant property variations of the same state (object) across different objects (states). To address this problem, existing approaches often adopt either all-to-one or one-to-one representation paradigms. Ho... | {
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2501.10696 | Algorithmic Derivation of Human Spatial Navigation Indices From Eye
Movement Data | [
"cs.HC",
"cs.AI"
] | Spatial navigation is a complex cognitive function involving sensory inputs, such as visual, auditory, and proprioceptive information, to understand and move within space. This ability allows humans to create mental maps, navigate through environments, and process directional cues, crucial for exploring new places and ... | {
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2501.10698 | An Interpretable Neural Control Network with Adaptable Online Learning
for Sample Efficient Robot Locomotion Learning | [
"cs.RO",
"cs.LG"
] | Robot locomotion learning using reinforcement learning suffers from training sample inefficiency and exhibits the non-understandable/black-box nature. Thus, this work presents a novel SME-AGOL to address such problems. Firstly, Sequential Motion Executor (SME) is a three-layer interpretable neural network, where the fi... | {
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2501.10700 | Subcodes of Second-Order Reed-Muller Codes via Recursive Subproducts | [
"cs.IT",
"math.IT"
] | We use a simple construction called `recursive subproducts' (that is known to yield good codes of lengths $n^m$, $n \geq 3$) to identify a family of codes sandwiched between first-order and second-order Reed-Muller (RM) codes. These codes are subcodes of multidimensional product codes that use first-order RM codes as c... | {
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2501.10705 | Secure Communication in Dynamic RDARS-Driven Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this letter, we investigate a dynamic reconfigurable distributed antenna and reflection surface (RDARS)-driven secure communication system, where the working mode of the RDARS can be flexibly configured. We aim to maximize the secrecy rate by jointly designing the active beamforming vectors, reflection coefficients,... | {
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2501.10709 | Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks
at ACM ICAIF FinRL Contest 2023-2024 | [
"cs.CE",
"cs.AI",
"stat.ML"
] | Reinforcement learning has demonstrated great potential for performing financial tasks. However, it faces two major challenges: policy instability and sampling bottlenecks. In this paper, we revisit ensemble methods with massively parallel simulations on graphics processing units (GPUs), significantly enhancing the com... | {
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2501.10711 | How Should We Build A Benchmark? Revisiting 274 Code-Related Benchmarks
For LLMs | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Various benchmarks have been proposed to assess the performance of large language models (LLMs) in different coding scenarios. We refer to them as code-related benchmarks. However, there are no systematic guidelines by which such a benchmark should be developed to ensure its quality, reliability, and reproducibility. W... | {
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2501.10712 | Poisson Hail on a Wireless Ground | [
"cs.IT",
"cs.NI",
"math.IT"
] | This paper defines a new model which incorporates three key ingredients of a large class of wireless communication systems: (1) spatial interactions through interference, (2) dynamics of the queueing type, with users joining and leaving, and (3) carrier sensing and collision avoidance as used in, e.g., WiFi. In systems... | {
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2501.10713 | Human-like Nonverbal Behavior with MetaHumans in Real-World Interaction
Studies: An Architecture Using Generative Methods and Motion Capture | [
"cs.HC",
"cs.RO"
] | Socially interactive agents are gaining prominence in domains like healthcare, education, and service contexts, particularly virtual agents due to their inherent scalability. To facilitate authentic interactions, these systems require verbal and nonverbal communication through e.g., facial expressions and gestures. Whi... | {
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2501.10714 | FSMoE: A Flexible and Scalable Training System for Sparse
Mixture-of-Experts Models | [
"cs.LG"
] | Recent large language models (LLMs) have tended to leverage sparsity to reduce computations, employing the sparsely activated mixture-of-experts (MoE) technique. MoE introduces four modules, including token routing, token communication, expert computation, and expert parallelism, that impact model quality and training ... | {
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2501.10722 | A Unified Regularization Approach to High-Dimensional Generalized Tensor
Bandits | [
"cs.LG",
"stat.ML"
] | Modern decision-making scenarios often involve data that is both high-dimensional and rich in higher-order contextual information, where existing bandits algorithms fail to generate effective policies. In response, we propose in this paper a generalized linear tensor bandits algorithm designed to tackle these challenge... | {
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2501.10727 | In the Picture: Medical Imaging Datasets, Artifacts, and their Living
Review | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the generalizability of algorithms and, consequently, negatively impact patient outcomes. While existing medical imaging literature reviews mostly fo... | {
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2501.10729 | Robust Local Polynomial Regression with Similarity Kernels | [
"stat.ME",
"cs.LG",
"stat.ML"
] | Local Polynomial Regression (LPR) is a widely used nonparametric method for modeling complex relationships due to its flexibility and simplicity. It estimates a regression function by fitting low-degree polynomials to localized subsets of the data, weighted by proximity. However, traditional LPR is sensitive to outlier... | {
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2501.10731 | Characterizing the Effects of Translation on Intertextuality using
Multilingual Embedding Spaces | [
"cs.CL"
] | Rhetorical devices are difficult to translate, but they are crucial to the translation of literary documents. We investigate the use of multilingual embedding spaces to characterize the preservation of intertextuality, one common rhetorical device, across human and machine translation. To do so, we use Biblical texts, ... | {
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2501.10733 | A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A
Case Study on Hepatocellular Carcinoma Prediction | [
"cs.CV"
] | Longitudinal MRI analysis is crucial for predicting disease outcomes, particularly in chronic conditions like hepatocellular carcinoma (HCC), where early detection can significantly influence treatment strategies and patient prognosis. Yet, due to challenges like limited data availability, subtle parenchymal changes, a... | {
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2501.10734 | GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented
Generation for Automatic Speech Recognition Systems | [
"eess.AS",
"cs.AI",
"cs.SD"
] | Automatic Speech Recognition (ASR) systems have demonstrated remarkable performance across various applications. However, limited data and the unique language features of specific domains, such as low-resource languages, significantly degrade their performance and lead to higher Word Error Rates (WER). In this study, w... | {
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2501.10736 | Semi-supervised Semantic Segmentation for Remote Sensing Images via
Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention | [
"cs.CV",
"cs.AI"
] | Semi-supervised learning offers an appealing solution for remote sensing (RS) image segmentation to relieve the burden of labor-intensive pixel-level labeling. However, RS images pose unique challenges, including rich multi-scale features and high inter-class similarity. To address these problems, this paper proposes a... | {
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2501.10739 | Computational Discovery of Chiasmus in Ancient Religious Text | [
"cs.CL"
] | Chiasmus, a debated literary device in Biblical texts, has captivated mystics while sparking ongoing scholarly discussion. In this paper, we introduce the first computational approach to systematically detect chiasmus within Biblical passages. Our method leverages neural embeddings to capture lexical and semantic patte... | {
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2501.10741 | Development of Application-Specific Large Language Models to Facilitate
Research Ethics Review | [
"cs.CL",
"cs.CY"
] | Institutional review boards (IRBs) play a crucial role in ensuring the ethical conduct of human subjects research, but face challenges including inconsistency, delays, and inefficiencies. We propose the development and implementation of application-specific large language models (LLMs) to facilitate IRB review processe... | {
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2501.10743 | Analysis of Age-Energy Trade-off in IoT Networks Using Stochastic
Geometry | [
"cs.IT",
"math.IT"
] | We study an internet of things (IoT) network where devices harvest energy from transmitter power. IoT devices use this harvested energy to operate and decode data packets. We propose a slot division scheme based on a parameter $\xi$, where the first phase is for energy harvesting (EH) and the second phase is for data t... | {
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2501.10750 | PEARL: Preconditioner Enhancement through Actor-critic Reinforcement
Learning | [
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] | We present PEARL (Preconditioner Enhancement through Actor-critic Reinforcement Learning), a novel approach to learning matrix preconditioners. Existing preconditioners such as Jacobi, Incomplete LU, and Algebraic Multigrid methods offer problem-specific advantages but rely heavily on hyperparameter tuning. Recent adva... | {
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2501.10752 | Quadcopter Position Hold Function using Optical Flow in a
Smartphone-based Flight Computer | [
"cs.CV"
] | Purpose. This paper explores the capability of smartphones as computing devices for a quadcopter, specifically in terms of the ability of drones to maintain a position known as the position hold function. Image processing can be performed with the phone's sensors and powerful built-in camera. Method. Using Shi-Tomasi c... | {
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2501.10753 | Pinching Antennas: Principles, Applications and Challenges | [
"cs.IT",
"eess.SP",
"math.IT"
] | Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. Howev... | {
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2501.10755 | An Experimental Study on Joint Modeling for Sound Event Localization and
Detection with Source Distance Estimation | [
"cs.SD",
"cs.LG",
"eess.AS"
] | In traditional sound event localization and detection (SELD) tasks, the focus is typically on sound event detection (SED) and direction-of-arrival (DOA) estimation, but they fall short of providing full spatial information about the sound source. The 3D SELD task addresses this limitation by integrating source distance... | {
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2501.10756 | D2D Coded Caching Schemes for Multiaccess Networks with Combinatorial
Access Topology | [
"cs.IT",
"math.IT"
] | This paper considers wireless device-to-device (D2D) coded caching in a multiaccess network, where the users communicate with each other and each user can access multiple cache nodes. Access topologies derived from two combinatorial designs known as the $t$-design and $t$-group divisible design ($t$-GDD), referred to a... | {
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2501.10757 | Deformable Image Registration of Dark-Field Chest Radiographs for Local
Lung Signal Change Assessment | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diagnosis of respiratory diseases. However, previous studies of dark-field chest radiographs evaluated the lung signal only in the inspiratory breathing state. Our work aims to... | {
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2501.10761 | Infrared and Visible Image Fusion: From Data Compatibility to Task
Adaption | [
"cs.CV"
] | Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representation. Since 2018, the field has entered the deep learning era, with an increasing variety of approaches introducing a range of networks and l... | {
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2501.10768 | MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science | [
"cs.AI"
] | Pre-trained on extensive text and image corpora, current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks. However, their performance is still lacking in physical domains that require understanding diagrams with complex physical structures and quantitative analys... | {
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2501.10770 | Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through
Explainable Uncertainty Bayesian Quantification | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Accurately classifying COVID-19 pneumonia in 3D CT scans remains a significant challenge in the field of medical image analysis. Although deterministic neural networks have shown promising results in this area, they provide only point estimates outputs yielding poor diagnostic in clinical decision-making. In this paper... | {
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2501.10774 | Model Monitoring in the Absence of Labeled Data via Feature Attributions
Distributions | [
"cs.LG"
] | Model monitoring involves analyzing AI algorithms once they have been deployed and detecting changes in their behaviour. This thesis explores machine learning model monitoring ML before the predictions impact real-world decisions or users. This step is characterized by one particular condition: the absence of labelled ... | {
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2501.10775 | MedFILIP: Medical Fine-grained Language-Image Pre-training | [
"cs.CV",
"cs.AI"
] | Medical vision-language pretraining (VLP) that leverages naturally-paired medical image-report data is crucial for medical image analysis. However, existing methods struggle to accurately characterize associations between images and diseases, leading to inaccurate or incomplete diagnostic results. In this work, we prop... | {
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2501.10777 | The working principles of model-based GAs fall within the PAC framework:
A mathematical theory of problem decomposition | [
"cs.NE"
] | The concepts of linkage, building blocks, and problem decomposition have long existed in the genetic algorithm (GA) field and have guided the development of model-based GAs for decades. However, their definitions are usually vague, making it difficult to develop theoretical support. This paper provides an algorithm-ind... | {
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2501.10781 | Simultaneous Computation with Multiple Prioritizations in Multi-Agent
Motion Planning | [
"cs.MA",
"cs.AI",
"cs.RO"
] | Multi-agent path finding (MAPF) in large networks is computationally challenging. An approach for MAPF is prioritized planning (PP), in which agents plan sequentially according to their priority. Albeit a computationally efficient approach for MAPF, the solution quality strongly depends on the prioritization. Most prio... | {
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2501.10782 | ML-SceGen: A Multi-level Scenario Generation Framework | [
"cs.AI"
] | Current scientific research witnesses various attempts at applying Large Language Models for scenario generation but is inclined only to comprehensive or dangerous scenarios. In this paper, we seek to build a three-stage framework that not only lets users regain controllability over the generated scenarios but also gen... | {
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2501.10784 | Measuring Fairness in Financial Transaction Machine Learning Models | [
"cs.LG"
] | Mastercard, a global leader in financial services, develops and deploys machine learning models aimed at optimizing card usage and preventing attrition through advanced predictive models. These models use aggregated and anonymized card usage patterns, including cross-border transactions and industry-specific spending, ... | {
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2501.10787 | LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval
and Highlight Detection | [
"cs.CV",
"cs.IR",
"cs.LG"
] | Video Moment Retrieval and Highlight Detection aim to find corresponding content in the video based on a text query. Existing models usually first use contrastive learning methods to align video and text features, then fuse and extract multimodal information, and finally use a Transformer Decoder to decode multimodal i... | {
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2501.10788 | Decoupling Appearance Variations with 3D Consistent Features in Gaussian
Splatting | [
"cs.CV"
] | Gaussian Splatting has emerged as a prominent 3D representation in novel view synthesis, but it still suffers from appearance variations, which are caused by various factors, such as modern camera ISPs, different time of day, weather conditions, and local light changes. These variations can lead to floaters and color d... | {
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2501.10789 | CS-Net:Contribution-based Sampling Network for Point Cloud
Simplification | [
"cs.CV"
] | Point cloud sampling plays a crucial role in reducing computation costs and storage requirements for various vision tasks. Traditional sampling methods, such as farthest point sampling, lack task-specific information and, as a result, cannot guarantee optimal performance in specific applications. Learning-based methods... | {
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2501.10791 | A Novel Precoder for Peak-to-Average Power Ratio Reduction in OTFS
Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | We consider the issue of high peak-to-average-power ratio (PAPR) of Orthogonal time frequency space (OTFS) modulated signals. This paper proposes a low-complexity novel iterative PAPR reduction method which achieves a PAPR reduction of roughly 5 dB when compared to a OTFS modulated signal without any PAPR compensation.... | {
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2501.10796 | Dynamic Trend Fusion Module for Traffic Flow Prediction | [
"cs.LG"
] | Accurate traffic flow prediction is essential for applications like transport logistics but remains challenging due to complex spatio-temporal correlations and non-linear traffic patterns. Existing methods often model spatial and temporal dependencies separately, failing to effectively fuse them. To overcome this limit... | {
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2501.10799 | Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary
Feedback | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs) have recently demonstrated remarkable success in mathematical reasoning. Despite progress in methods like chain-of-thought prompting and self-consistency sampling, these advances often focus on final correctness without ensuring that the underlying reasoning process is coherent and reliable... | {
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2501.10800 | Jailbreaking Large Language Models in Infinitely Many Ways | [
"cs.LG",
"cs.CR"
] | We discuss the "Infinitely Many Meanings" attacks (IMM), a category of jailbreaks that leverages the increasing capabilities of a model to handle paraphrases and encoded communications to bypass their defensive mechanisms. IMMs' viability pairs and grows with a model's capabilities to handle and bind the semantics of s... | {
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2501.10806 | Non-Expansive Mappings in Two-Time-Scale Stochastic Approximation:
Finite-Time Analysis | [
"math.OC",
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | Two-time-scale stochastic approximation is an iterative algorithm used in applications such as optimization, reinforcement learning, and control. Finite-time analysis of these algorithms has primarily focused on fixed point iterations where both time-scales have contractive mappings. In this paper, we study two-time-sc... | {
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2501.10808 | Optimizing MACD Trading Strategies A Dance of Finance, Wavelets, and
Genetics | [
"cs.CE"
] | In today's financial markets, quantitative trading has become an essential trading method, with the MACD indicator widely employed in quantitative trading strategies. This paper begins by screening and cleaning the dataset, establishing a model that adheres to the basic buy and sell rules of the MACD, and calculating k... | {
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2501.10809 | Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using
Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach | [
"cs.CV",
"cs.AI"
] | The rapid growth of AI in poultry farming has highlighted the challenge of efficiently labeling large, diverse datasets. Manual annotation is time-consuming, making it impractical for modern systems that continuously generate data. This study explores semi-supervised auto-labeling methods, integrating active learning, ... | {
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2501.10810 | Convergence and Running Time of Time-dependent Ant Colony Algorithms | [
"cs.DS",
"cs.NE"
] | Ant Colony Optimization (ACO) is a well-known method inspired by the foraging behavior of ants and is extensively used to solve combinatorial optimization problems. In this paper, we first consider a general framework based on the concept of a construction graph - a graph associated with an instance of the optimization... | {
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2501.10812 | Graph Coloring to Reduce Computation Time in Prioritized Planning | [
"cs.MA",
"cs.AI",
"cs.RO"
] | Distributing computations among agents in large networks reduces computational effort in multi-agent path finding (MAPF). One distribution strategy is prioritized planning (PP). In PP, we couple and prioritize interacting agents to achieve a desired behavior across all agents in the network. We characterize the interac... | {
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2501.10814 | No More Sliding Window: Efficient 3D Medical Image Segmentation with
Differentiable Top-k Patch Sampling | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | 3D models are favored over 2D for 3D medical image segmentation tasks due to their ability to leverage inter-slice relationship, yielding higher segmentation accuracy. However, 3D models demand significantly more GPU memory with increased model size and intermediate tensors. A common solution is to use patch-based trai... | {
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2501.10815 | An Interpretable Measure for Quantifying Predictive Dependence between
Continuous Random Variables -- Extended Version | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | A fundamental task in statistical learning is quantifying the joint dependence or association between two continuous random variables. We introduce a novel, fully non-parametric measure that assesses the degree of association between continuous variables $X$ and $Y$, capable of capturing a wide range of relationships, ... | {
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2501.10819 | GAUDA: Generative Adaptive Uncertainty-guided Diffusion-based
Augmentation for Surgical Segmentation | [
"cs.CV",
"cs.LG"
] | Augmentation by generative modelling yields a promising alternative to the accumulation of surgical data, where ethical, organisational and regulatory aspects must be considered. Yet, the joint synthesis of (image, mask) pairs for segmentation, a major application in surgery, is rather unexplored. We propose to learn s... | {
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2501.10822 | Addressing Multilabel Imbalance with an Efficiency-Focused Approach
Using Diffusion Model-Generated Synthetic Samples | [
"cs.LG",
"cs.AI"
] | Predictive models trained on imbalanced data tend to produce biased results. This problem is exacerbated when there is not just one output label, but a set of them. This is the case for multilabel learning (MLL) algorithms used to classify patterns, rank labels, or learn the distribution of outputs. Many solutions have... | {
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2501.10824 | Information Content and Entropy of Finite Patterns from a Combinatorial
Perspective | [
"cs.IT",
"cs.DM",
"math.IT"
] | A unified combinatorial definition of the information content and entropy of different types of patterns, compatible with the traditional concepts of information and entropy, going beyond the limitations of Shannon information interpretable for ergodic Markov processes. We compare the information content of various fin... | {
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2501.10825 | Statistical Design of Thermal Protection System Using Physics-Informed
Machine learning | [
"cs.CE"
] | Estimating the material properties of thermal protection films is crucial for their effective design and application, particularly in high-temperature environments. This work presents a novel approach to determine the properties using uncertainty quantification simulations. We quantify uncertainty in the material prope... | {
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2501.10827 | Integrating Expert and Physics Knowledge for Modeling Heat Load in
District Heating Systems | [
"eess.SY",
"cs.SY"
] | New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability and satisfying user requirements. In this paper, we present HELIOS, a dedicated artificial intelligence (AI) model designed specifically for m... | {
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2501.10834 | Visual RAG: Expanding MLLM visual knowledge without fine-tuning | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Multimodal Large Language Models (MLLMs) have achieved notable performance in computer vision tasks that require reasoning across visual and textual modalities, yet their capabilities are limited to their pre-trained data, requiring extensive fine-tuning for updates. Recent researches have explored the use of In-Contex... | {
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2501.10835 | Anatomy of a Historic Blackout: Decoding Spatiotemporal Dynamics of
Power Outages and Disparities During Hurricane Beryl | [
"cs.CE"
] | This study investigates the spatial patterns and temporal variations in outage duration, intensity, and restoration/recovery following the 2024 Hurricane Beryl in Houston, Texas. This historic blackout caused widespread power disruptions across the Houston metropolitan area, leaving more than 2 million customers withou... | {
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2501.10836 | BAP v2: An Enhanced Task Framework for Instruction Following in
Minecraft Dialogues | [
"cs.CL",
"cs.AI"
] | Interactive agents capable of understanding and executing instructions in the physical world have long been a central goal in AI research. The Minecraft Collaborative Building Task (MCBT) provides one such setting to work towards this goal (Narayan-Chen, Jayannavar, and Hockenmaier 2019). It is a two-player game in whi... | {
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2501.10839 | Systems Engineering for Autonomous Vehicles; Supervising AI using Large
Language Models (SSuperLLM) | [
"eess.SY",
"cs.SY"
] | Generative Artificial Intelligence (GAI) and the idea to use hierarchical models has been around for some years now. GAI has proved to be an extremely useful tool for Autonomous Vehicles (AVs). AVs need to perform robustly in their environment. Thus the AV behavior and short-term trajectory planning needs to be: a) des... | {
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2501.10841 | Practical and Ready-to-Use Methodology to Assess the re-identification
Risk in Anonymized Datasets | [
"cs.CR",
"cs.AI",
"cs.DB"
] | To prove that a dataset is sufficiently anonymized, many privacy policies suggest that a re-identification risk assessment be performed, but do not provide a precise methodology for doing so, leaving the industry alone with the problem. This paper proposes a practical and ready-to-use methodology for re-identification ... | {
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2501.10842 | BOOST: Microgrid Sizing using Ordinal Optimization | [
"eess.SY",
"cs.SY"
] | The transition to sustainable energy systems has highlighted the critical need for efficient sizing of renewable energy resources in microgrids. In particular, designing photovoltaic (PV) and battery systems to meet residential loads is challenging due to trade-offs between cost, reliability, and environmental impact. ... | {
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2501.10848 | Fake Advertisements Detection Using Automated Multimodal Learning: A
Case Study for Vietnamese Real Estate Data | [
"cs.LG",
"cs.AI"
] | The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we pro... | {
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2501.10851 | Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction | [
"eess.IV",
"cs.CV"
] | Reconstructing MR images using deep neural networks from undersampled k-space data without using fully sampled training references offers significant value in practice, which is a self-supervised regression problem calling for effective prior knowledge and supervision. The Siamese architectures are motivated by the def... | {
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2501.10854 | Achievable DoF Bounds for Cache-Aided Asymmetric MIMO Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | Integrating coded caching (CC) into multiple-input multiple-output (MIMO) communications can significantly enhance the achievable degrees of freedom (DoF) in wireless networks. This paper investigates a practical cache-aided asymmetric MIMO configuration with cache ratio $\gamma$, where a server equipped with $L$ trans... | {
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2501.10857 | Learning Nonverbal Cues in Multiparty Social Interactions for Robotic
Facilitators | [
"cs.RO",
"cs.LG"
] | Conventional behavior cloning (BC) models often struggle to replicate the subtleties of human actions. Previous studies have attempted to address this issue through the development of a new BC technique: Implicit Behavior Cloning (IBC). This new technique consistently outperformed the conventional Mean Squared Error (M... | {
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2501.10858 | Reliable Text-to-SQL with Adaptive Abstention | [
"cs.DB",
"cs.AI"
] | Large language models (LLMs) have revolutionized natural language interfaces for databases, particularly in text-to-SQL conversion. However, current approaches often generate unreliable outputs when faced with ambiguity or insufficient context. We present Reliable Text-to-SQL (RTS), a novel framework that enhances quer... | {
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2501.10859 | Which price to pay? Auto-tuning building MPC controller for optimal
economic cost | [
"eess.SY",
"cs.LG",
"cs.SY",
"math.OC"
] | Model predictive control (MPC) controller is considered for temperature management in buildings but its performance heavily depends on hyperparameters. Consequently, MPC necessitates meticulous hyperparameter tuning to attain optimal performance under diverse contracts. However, conventional building controller design ... | {
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} |
2501.10860 | Zero-shot and Few-shot Learning with Instruction-following LLMs for
Claim Matching in Automated Fact-checking | [
"cs.CL",
"cs.AI"
] | The claim matching (CM) task can benefit an automated fact-checking pipeline by putting together claims that can be resolved with the same fact-check. In this work, we are the first to explore zero-shot and few-shot learning approaches to the task. We consider CM as a binary classification task and experiment with a se... | {
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} |
2501.10861 | Dynamic Continual Learning: Harnessing Parameter Uncertainty for
Improved Network Adaptation | [
"cs.LG",
"cs.AI"
] | When fine-tuning Deep Neural Networks (DNNs) to new data, DNNs are prone to overwriting network parameters required for task-specific functionality on previously learned tasks, resulting in a loss of performance on those tasks. We propose using parameter-based uncertainty to determine which parameters are relevant to a... | {
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} |
2501.10866 | QGAPHEnsemble : Combining Hybrid QLSTM Network Ensemble via Adaptive
Weighting for Short Term Weather Forecasting | [
"cs.LG"
] | Accurate weather forecasting holds significant importance, serving as a crucial tool for decision-making in various industrial sectors. The limitations of statistical models, assuming independence among data points, highlight the need for advanced methodologies. The correlation between meteorological variables necessit... | {
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} |
2501.10868 | Generating Structured Outputs from Language Models: Benchmark and
Studies | [
"cs.CL",
"cs.AI"
] | Reliably generating structured outputs has become a critical capability for modern language model (LM) applications. Constrained decoding has emerged as the dominant technology across sectors for enforcing structured outputs during generation. Despite its growing adoption, little has been done with the systematic evalu... | {
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} |
2501.10869 | Diffusion-Based Imitation Learning for Social Pose Generation | [
"cs.LG",
"cs.RO"
] | Intelligent agents, such as robots and virtual agents, must understand the dynamics of complex social interactions to interact with humans. Effectively representing social dynamics is challenging because we require multi-modal, synchronized observations to understand a scene. We explore how using a single modality, the... | {
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} |
2501.10870 | Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept
Shifts in Nonparametric Regression | [
"stat.ML",
"cs.LG"
] | When concept shifts and sample scarcity are present in the target domain of interest, nonparametric regression learners often struggle to generalize effectively. The technique of transfer learning remedies these issues by leveraging data or pre-trained models from similar source domains. While existing generalization a... | {
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} |
2501.10871 | Enhancing User Intent for Recommendation Systems via Large Language
Models | [
"cs.IR"
] | Recommendation systems play a critical role in enhancing user experience and engagement in various online platforms. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), rely heavily on past user interactions or item features. However, these models often fail to capture the dynam... | {
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} |
2501.10875 | RIS Deployment Optimization with Iterative Detection and Decoding in
Multiuser Multiple-Antenna Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | This work investigates a Reconfigurable Intelligent Surface (RIS)-assisted uplink system employing iterative detection and decoding (IDD) techniques. We analyze the impact of tuning system parameter tuning for several deployment configurations, including the number of users, access point (AP) antennas, and RIS elements... | {
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} |
2501.10876 | Certifying Robustness via Topological Representations | [
"stat.ML",
"cs.CG",
"cs.LG"
] | We propose a neural network architecture that can learn discriminative geometric representations of data from persistence diagrams, common descriptors of Topological Data Analysis. The learned representations enjoy Lipschitz stability with a controllable Lipschitz constant. In adversarial learning, this stability can b... | {
"Other": 1,
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} |
2501.10877 | Distributed Quasi-Newton Method for Fair and Fast Federated Learning | [
"cs.LG"
] | Federated learning (FL) is a promising technology that enables edge devices/clients to collaboratively and iteratively train a machine learning model under the coordination of a central server. The most common approach to FL is first-order methods, where clients send their local gradients to the server in each iteratio... | {
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} |
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