id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.03654 | Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning
Dynamics | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Activation functions are fundamental elements of deep learning architectures as they significantly influence training dynamics. ReLU, while widely used, is prone to the dying neuron problem, which has been mitigated by variants such as LeakyReLU, PReLU, and ELU that better handle negative neuron outputs. Recently, self... | {
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2502.03656 | A Study in Dataset Distillation for Image Super-Resolution | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Dataset distillation is the concept of condensing large datasets into smaller but highly representative synthetic samples. While previous research has primarily focused on image classification, its application to image Super-Resolution (SR) remains underexplored. This exploratory work studies multiple dataset distillat... | {
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2502.03658 | Advancing Weight and Channel Sparsification with Enhanced Saliency | [
"cs.LG",
"cs.CV"
] | Pruning aims to accelerate and compress models by removing redundant parameters, identified by specifically designed importance scores which are usually imperfect. This removal is irreversible, often leading to subpar performance in pruned models. Dynamic sparse training, while attempting to adjust sparse structures du... | {
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2502.03660 | Energy & Force Regression on DFT Trajectories is Not Enough for
Universal Machine Learning Interatomic Potentials | [
"cond-mat.mtrl-sci",
"cs.AI",
"cs.LG"
] | Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated simulations for materials discovery. However, current research efforts fail to impactfully utilize MLIPs due to: 1. Overreliance on Density Functional Theory (DFT) for MLIP training data creation; 2. MLIPs' inability to reliably and accuratel... | {
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2502.03662 | EC-SBM Synthetic Network Generator | [
"cs.SI"
] | Generating high-quality synthetic networks with realistic community structure is vital to effectively evaluate community detection algorithms. In this study, we propose a new synthetic network generator called the Edge-Connected Stochastic Block Model (EC-SBM). The goal of EC-SBM is to take a given clustered real-world... | {
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2502.03664 | Contrastive Learning for Cold Start Recommendation with Adaptive Feature
Fusion | [
"cs.IR",
"cs.LG"
] | This paper proposes a cold start recommendation model that integrates contrastive learning, aiming to solve the problem of performance degradation of recommendation systems in cold start scenarios due to the scarcity of user and item interaction data. The model dynamically adjusts the weights of key features through an... | {
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2502.03668 | Privacy-Preserving Generative Models: A Comprehensive Survey | [
"cs.LG",
"cs.CR"
] | Despite the generative model's groundbreaking success, the need to study its implications for privacy and utility becomes more urgent. Although many studies have demonstrated the privacy threats brought by GANs, no existing survey has systematically categorized the privacy and utility perspectives of GANs and VAEs. In ... | {
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2502.03669 | Unrealized Expectations: Comparing AI Methods vs Classical Algorithms
for Maximum Independent Set | [
"cs.LG",
"cs.AI",
"cs.DM",
"math.OC",
"stat.ML"
] | AI methods, such as generative models and reinforcement learning, have recently been applied to combinatorial optimization (CO) problems, especially NP-hard ones. This paper compares such GPU-based methods with classical CPU-based methods on Maximum Independent Set (MIS). Experiments on standard graph families show tha... | {
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2502.03670 | Chaos into Order: Neural Framework for Expected Value Estimation of
Stochastic Partial Differential Equations | [
"cs.LG"
] | Stochastic Partial Differential Equations (SPDEs) are fundamental to modeling complex systems in physics, finance, and engineering, yet their numerical estimation remains a formidable challenge. Traditional methods rely on discretization, introducing computational inefficiencies, and limiting applicability in high-dime... | {
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2502.03671 | Advancing Reasoning in Large Language Models: Promising Methods and
Approaches | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have succeeded remarkably in various natural language processing (NLP) tasks, yet their reasoning capabilities remain a fundamental challenge. While LLMs exhibit impressive fluency and factual recall, their ability to perform complex reasoning-spanning logical deduction, mathematical proble... | {
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2502.03672 | Physically consistent predictive reduced-order modeling by enhancing
Operator Inference with state constraints | [
"physics.comp-ph",
"cs.LG",
"cs.NA",
"math.NA"
] | Numerical simulations of complex multiphysics systems, such as char combustion considered herein, yield numerous state variables that inherently exhibit physical constraints. This paper presents a new approach to augment Operator Inference -- a methodology within scientific machine learning that enables learning from d... | {
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2502.03674 | An Empirical Study of Methods for Small Object Detection from Satellite
Imagery | [
"cs.CV",
"cs.AI"
] | This paper reviews object detection methods for finding small objects from remote sensing imagery and provides an empirical evaluation of four state-of-the-art methods to gain insights into method performance and technical challenges. In particular, we use car detection from urban satellite images and bee box detection... | {
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2502.03676 | Anytime Planning for End-Effector Trajectory Tracking | [
"cs.RO"
] | End-effector trajectory tracking algorithms find joint motions that drive robot manipulators to track reference trajectories. In practical scenarios, anytime algorithms are preferred for their ability to quickly generate initial motions and continuously refine them over time. In this paper, we present an algorithmic fr... | {
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2502.03678 | Reflection-Window Decoding: Text Generation with Selective Refinement | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The autoregressive decoding for text generation in large language models (LLMs), while widely used, is inherently suboptimal due to the lack of a built-in mechanism to perform refinement and/or correction of the generated content. In this paper, we consider optimality in terms of the joint probability over the generate... | {
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2502.03681 | On the effects of angular acceleration in orientation estimation using
inertial measurement units | [
"eess.SY",
"cs.SY"
] | Determining the orientation of a rigid body using an inertial measurement unit is a common problem in many engineering applications. However, sensor fusion algorithms suffer from performance loss when other motions besides the gravitational acceleration affect the accelerometer. In this paper, we show that linear accel... | {
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2502.03685 | Controlled LLM Decoding via Discrete Auto-regressive Biasing | [
"cs.CL",
"cs.LG",
"stat.ML"
] | Controlled text generation allows for enforcing user-defined constraints on large language model outputs, an increasingly important field as LLMs become more prevalent in everyday life. One common approach uses energy-based decoding, which defines a target distribution through an energy function that combines multiple ... | {
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2502.03686 | Variational Control for Guidance in Diffusion Models | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | Diffusion models exhibit excellent sample quality, but existing guidance methods often require additional model training or are limited to specific tasks. We revisit guidance in diffusion models from the perspective of variational inference and control, introducing Diffusion Trajectory Matching (DTM) that enables guidi... | {
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2502.03687 | Conditional Diffusion Models are Medical Image Classifiers that Provide
Explainability and Uncertainty for Free | [
"cs.CV",
"cs.LG"
] | Discriminative classifiers have become a foundational tool in deep learning for medical imaging, excelling at learning separable features of complex data distributions. However, these models often need careful design, augmentation, and training techniques to ensure safe and reliable deployment. Recently, diffusion mode... | {
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2502.03688 | A Comparison of DeepSeek and Other LLMs | [
"cs.CL",
"cs.AI"
] | Recently, DeepSeek has been the focus of attention in and beyond the AI community. An interesting problem is how DeepSeek compares to other large language models (LLMs). There are many tasks an LLM can do, and in this paper, we use the task of predicting an outcome using a short text for comparison. We consider two set... | {
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2502.03692 | DocMIA: Document-Level Membership Inference Attacks against DocVQA
Models | [
"cs.LG",
"cs.CL",
"cs.CR"
] | Document Visual Question Answering (DocVQA) has introduced a new paradigm for end-to-end document understanding, and quickly became one of the standard benchmarks for multimodal LLMs. Automating document processing workflows, driven by DocVQA models, presents significant potential for many business sectors. However, do... | {
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2502.03695 | Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC
Local Trajectory Planning Method | [
"cs.RO",
"cs.SY",
"eess.SY"
] | The widespread application of autonomous driving technology has significantly advanced the field of autonomous racing. Model Predictive Contouring Control (MPCC) is a highly effective local trajectory planning method for autonomous racing. However, the traditional MPCC method struggles with racetracks that have signifi... | {
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2502.03696 | Cascaded Learned Bloom Filter for Optimal Model-Filter Size Balance and
Fast Rejection | [
"cs.DS",
"cs.CC",
"cs.LG"
] | Recent studies have demonstrated that learned Bloom filters, which combine machine learning with the classical Bloom filter, can achieve superior memory efficiency. However, existing learned Bloom filters face two critical unresolved challenges: the balance between the machine learning model size and the Bloom filter s... | {
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2502.03698 | How vulnerable is my policy? Adversarial attacks on modern behavior
cloning policies | [
"cs.LG",
"cs.CR",
"cs.RO"
] | Learning from Demonstration (LfD) algorithms have shown promising results in robotic manipulation tasks, but their vulnerability to adversarial attacks remains underexplored. This paper presents a comprehensive study of adversarial attacks on both classic and recently proposed algorithms, including Behavior Cloning (BC... | {
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2502.03699 | LLM Alignment as Retriever Optimization: An Information Retrieval
Perspective | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Large Language Models (LLMs) have revolutionized artificial intelligence with capabilities in reasoning, coding, and communication, driving innovation across industries. Their true potential depends on effective alignment to ensure correct, trustworthy and ethical behavior, addressing challenges like misinformation, ha... | {
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2502.03701 | First-ish Order Methods: Hessian-aware Scalings of Gradient Descent | [
"math.OC",
"cs.LG"
] | Gradient descent is the primary workhorse for optimizing large-scale problems in machine learning. However, its performance is highly sensitive to the choice of the learning rate. A key limitation of gradient descent is its lack of natural scaling, which often necessitates expensive line searches or heuristic tuning to... | {
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2502.03703 | On the Expressive Power of Subgraph Graph Neural Networks for Graphs
with Bounded Cycles | [
"cs.LG"
] | Graph neural networks (GNNs) have been widely used in graph-related contexts. It is known that the separation power of GNNs is equivalent to that of the Weisfeiler-Lehman (WL) test; hence, GNNs are imperfect at identifying all non-isomorphic graphs, which severely limits their expressive power. This work investigates $... | {
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2502.03708 | Aggregate and conquer: detecting and steering LLM concepts by combining
nonlinear predictors over multiple layers | [
"cs.CL",
"cs.AI",
"stat.ML"
] | A trained Large Language Model (LLM) contains much of human knowledge. Yet, it is difficult to gauge the extent or accuracy of that knowledge, as LLMs do not always ``know what they know'' and may even be actively misleading. In this work, we give a general method for detecting semantic concepts in the internal activat... | {
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2502.03711 | MultiQ&A: An Analysis in Measuring Robustness via Automated
Crowdsourcing of Question Perturbations and Answers | [
"cs.CL",
"cs.AI",
"cs.LG"
] | One critical challenge in the institutional adoption journey of Large Language Models (LLMs) stems from their propensity to hallucinate in generated responses. To address this, we propose MultiQ&A, a systematic approach for evaluating the robustness and consistency of LLM-generated answers. We demonstrate MultiQ&A's ab... | {
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2502.03714 | Universal Sparse Autoencoders: Interpretable Cross-Model Concept
Alignment | [
"cs.CV",
"cs.LG"
] | We present Universal Sparse Autoencoders (USAEs), a framework for uncovering and aligning interpretable concepts spanning multiple pretrained deep neural networks. Unlike existing concept-based interpretability methods, which focus on a single model, USAEs jointly learn a universal concept space that can reconstruct an... | {
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2502.03715 | Boosting Knowledge Graph-based Recommendations through Confidence-Aware
Augmentation with Large Language Models | [
"cs.IR",
"cs.AI"
] | Knowledge Graph-based recommendations have gained significant attention due to their ability to leverage rich semantic relationships. However, constructing and maintaining Knowledge Graphs (KGs) is resource-intensive, and the accuracy of KGs can suffer from noisy, outdated, or irrelevant triplets. Recent advancements i... | {
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2502.03717 | Efficiently Generating Expressive Quadruped Behaviors via
Language-Guided Preference Learning | [
"cs.RO",
"cs.AI"
] | Expressive robotic behavior is essential for the widespread acceptance of robots in social environments. Recent advancements in learned legged locomotion controllers have enabled more dynamic and versatile robot behaviors. However, determining the optimal behavior for interactions with different users across varied sce... | {
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2502.03721 | Detecting Backdoor Attacks via Similarity in Semantic Communication
Systems | [
"cs.CR",
"cs.LG"
] | Semantic communication systems, which leverage Generative AI (GAI) to transmit semantic meaning rather than raw data, are poised to revolutionize modern communications. However, they are vulnerable to backdoor attacks, a type of poisoning manipulation that embeds malicious triggers into training datasets. As a result, ... | {
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2502.03723 | Speaking the Language of Teamwork: LLM-Guided Credit Assignment in
Multi-Agent Reinforcement Learning | [
"cs.MA"
] | Credit assignment, the process of attributing credit or blame to individual agents for their contributions to a team's success or failure, remains a fundamental challenge in multi-agent reinforcement learning (MARL), particularly in environments with sparse rewards. Commonly-used approaches such as value decomposition ... | {
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2502.03724 | MD-BERT: Action Recognition in Dark Videos via Dynamic Multi-Stream
Fusion and Temporal Modeling | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.LG",
"cs.MM"
] | Action recognition in dark, low-light (under-exposed) or noisy videos is a challenging task due to visibility degradation, which can hinder critical spatiotemporal details. This paper proposes MD-BERT, a novel multi-stream approach that integrates complementary pre-processing techniques such as gamma correction and his... | {
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2502.03725 | Optimal Control of Fluid Restless Multi-armed Bandits: A Machine
Learning Approach | [
"cs.LG"
] | We propose a machine learning approach to the optimal control of fluid restless multi-armed bandits (FRMABs) with state equations that are either affine or quadratic in the state variables. By deriving fundamental properties of FRMAB problems, we design an efficient machine learning based algorithm. Using this algorith... | {
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2502.03726 | DICE: Distilling Classifier-Free Guidance into Text Embeddings | [
"cs.CV"
] | Text-to-image diffusion models are capable of generating high-quality images, but these images often fail to align closely with the given text prompts. Classifier-free guidance (CFG) is a popular and effective technique for improving text-image alignment in the generative process. However, using CFG introduces signific... | {
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2502.03729 | Action-Free Reasoning for Policy Generalization | [
"cs.RO",
"cs.AI"
] | End-to-end imitation learning offers a promising approach for training robot policies. However, generalizing to new settings remains a significant challenge. Although large-scale robot demonstration datasets have shown potential for inducing generalization, they are resource-intensive to scale. In contrast, human video... | {
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2502.03737 | Mitigating the Participation Bias by Balancing Extreme Ratings | [
"cs.LG",
"cs.GT"
] | Rating aggregation plays a crucial role in various fields, such as product recommendations, hotel rankings, and teaching evaluations. However, traditional averaging methods can be affected by participation bias, where some raters do not participate in the rating process, leading to potential distortions. In this paper,... | {
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2502.03738 | Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More | [
"cs.CV"
] | Since the introduction of Vision Transformer (ViT), patchification has long been regarded as a de facto image tokenization approach for plain visual architectures. By compressing the spatial size of images, this approach can effectively shorten the token sequence and reduce the computational cost of ViT-like plain arch... | {
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2502.03740 | Multiple Invertible and Partial-Equivariant Function for Latent Vector
Transformation to Enhance Disentanglement in VAEs | [
"cs.LG",
"cs.AI"
] | Disentanglement learning is a core issue for understanding and re-using trained information in Variational AutoEncoder (VAE), and effective inductive bias has been reported as a key factor. However, the actual implementation of such bias is still vague. In this paper, we propose a novel method, called Multiple Invertib... | {
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2502.03746 | Brain Tumor Identification using Improved YOLOv8 | [
"cs.CV",
"cs.LG"
] | Identifying the extent of brain tumors is a significant challenge in brain cancer treatment. The main difficulty is in the approximate detection of tumor size. Magnetic resonance imaging (MRI) has become a critical diagnostic tool. However, manually detecting the boundaries of brain tumors from MRI scans is a labor-int... | {
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2502.03748 | Rethinking the Residual Distribution of Locate-then-Editing Methods in
Model Editing | [
"cs.CL"
] | Model editing is a powerful technique for updating the knowledge of Large Language Models (LLMs). Locate-then-edit methods are a popular class of approaches that first identify the critical layers storing knowledge, then compute the residual of the last critical layer based on the edited knowledge, and finally perform ... | {
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2502.03749 | PINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal
Transport | [
"cs.LG",
"math.OC"
] | Optimal transport (OT) is a critical problem in optimization and machine learning, where accuracy and efficiency are paramount. Although entropic regularization and the Sinkhorn algorithm improve scalability, they frequently encounter numerical instability and slow convergence, especially when the regularization parame... | {
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2502.03750 | Principal Curvatures Estimation with Applications to Single Cell Data | [
"cs.LG",
"cs.AI"
] | The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracti... | {
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2502.03752 | PRISM: A Robust Framework for Skill-based Meta-Reinforcement Learning
with Noisy Demonstrations | [
"cs.LG",
"cs.AI"
] | Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based approaches tackle this by decomposing state-action sequences into reusable skills and employing hierarchical decision-making. However, these methods are highly susceptible to... | {
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2502.03755 | Regularization via f-Divergence: An Application to Multi-Oxide
Spectroscopic Analysis | [
"cs.LG"
] | In this paper, we address the task of characterizing the chemical composition of planetary surfaces using convolutional neural networks (CNNs). Specifically, we seek to predict the multi-oxide weights of rock samples based on spectroscopic data collected under Martian conditions. We frame this problem as a multi-target... | {
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2502.03758 | Improving Adversarial Robustness via Phase and Amplitude-aware Prompting | [
"cs.CV"
] | Deep neural networks are found to be vulnerable to adversarial noises. The prompt-based defense has been increasingly studied due to its high efficiency. However, existing prompt-based defenses mainly exploited mixed prompt patterns, where critical patterns closely related to object semantics lack sufficient focus. The... | {
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2502.03760 | RAMOTS: A Real-Time System for Aerial Multi-Object Tracking based on
Deep Learning and Big Data Technology | [
"cs.CV"
] | Multi-object tracking (MOT) in UAV-based video is challenging due to variations in viewpoint, low resolution, and the presence of small objects. While other research on MOT dedicated to aerial videos primarily focuses on the academic aspect by developing sophisticated algorithms, there is a lack of attention to the pra... | {
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2502.03762 | Learning Reward Machines from Partially Observed Optimal Policies | [
"cs.LG",
"cs.FL"
] | Inverse reinforcement learning is the problem of inferring a reward function from an optimal policy. In this work, it is assumed that the reward is expressed as a reward machine whose transitions depend on atomic propositions associated with the state of a Markov Decision Process (MDP). Our goal is to identify the true... | {
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2502.03765 | Replacing K-infinity Function with Leaky ReLU in Barrier Function
Design: A Union of Invariant Sets Approach for ReLU-Based Dynamical Systems | [
"eess.SY",
"cs.SY"
] | In this paper, a systematic framework is presented for determining piecewise affine PWA barrier functions and their corresponding invariant sets for dynamical systems identified via Rectified Linear Unit (ReLU) neural networks or their equivalent PWA representations. A common approach to determining the invariant set i... | {
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2502.03766 | Hierarchical Contextual Manifold Alignment for Structuring Latent
Representations in Large Language Models | [
"cs.CL"
] | The organization of latent token representations plays a crucial role in determining the stability, generalization, and contextual consistency of language models, yet conventional approaches to embedding refinement often rely on parameter modifications that introduce additional computational overhead. A hierarchical al... | {
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2502.03771 | Adaptive Semantic Prompt Caching with VectorQ | [
"cs.LG",
"cs.CL"
] | Semantic prompt caches reduce the latency and cost of large language model (LLM) inference by reusing cached LLM-generated responses for semantically similar prompts. Vector similarity metrics assign a numerical score to quantify the similarity between an embedded prompt and its nearest neighbor in the cache. Existing ... | {
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2502.03772 | A Retrospective Systematic Study on Hierarchical Sparse Query
Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma | [
"cs.CV",
"cs.AI"
] | Hepatocellular carcinoma (HCC) ranks as the third leading cause of cancer-related mortality worldwide, with early detection being crucial for improving patient survival rates. However, early screening for HCC using ultrasound suffers from insufficient sensitivity and is highly dependent on the expertise of radiologists... | {
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2502.03773 | ExpProof : Operationalizing Explanations for Confidential Models with
ZKPs | [
"cs.LG",
"cs.AI",
"cs.CR"
] | In principle, explanations are intended as a way to increase trust in machine learning models and are often obligated by regulations. However, many circumstances where these are demanded are adversarial in nature, meaning the involved parties have misaligned interests and are incentivized to manipulate explanations for... | {
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2502.03774 | High-Rate Spatially Coupled LDPC Codes Based on Massey's Convolutional
Self-Orthogonal Codes | [
"cs.IT",
"math.IT"
] | In this paper, we study a new class of high-rate spatially coupled LDPC (SC-LDPC) codes based on the convolutional self-orthogonal codes (CSOCs) first introduced by Massey. The SC-LDPC codes are constructed by treating the irregular graph corresponding to the parity-check matrix of a systematic rate R = (n - 1)/n CSOC ... | {
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2502.03776 | StarMAP: Global Neighbor Embedding for Faithful Data Visualization | [
"cs.LG"
] | Neighbor embedding is widely employed to visualize high-dimensional data; however, it frequently overlooks the global structure, e.g., intercluster similarities, thereby impeding accurate visualization. To address this problem, this paper presents Star-attracted Manifold Approximation and Projection (StarMAP), which in... | {
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2502.03777 | Multi-Label Test-Time Adaptation with Bound Entropy Minimization | [
"cs.CV"
] | Mainstream test-time adaptation (TTA) techniques endeavor to mitigate distribution shifts via entropy minimization for multi-class classification, inherently increasing the probability of the most confident class. However, when encountering multi-label instances, the primary challenge stems from the varying number of l... | {
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2502.03781 | Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound
Image Segmentation | [
"cs.CV",
"eess.IV"
] | Domain adaptation (DA) for cardiac ultrasound image segmentation is clinically significant and valuable. However, previous domain adaptation methods are prone to be affected by the incomplete pseudo-label and low-quality target to source images. Human-centric domain adaptation has great advantages of human cognitive gu... | {
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2502.03783 | UltraBones100k: An Ultrasound Image Dataset with CT-Derived Labels for
Lower Extremity Long Bone Surface Segmentation | [
"eess.IV",
"cs.CV"
] | Ultrasound-based bone surface segmentation is crucial in computer-assisted orthopedic surgery. However, ultrasound images have limitations, including a low signal-to-noise ratio, and acoustic shadowing, which make interpretation difficult. Existing deep learning models for bone segmentation rely primarily on costly man... | {
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2502.03785 | Reed-Muller Codes on CQ Channels via a New Correlation Bound for Quantum
Observables | [
"cs.IT",
"math.IT",
"quant-ph"
] | The question of whether Reed-Muller (RM) codes achieve capacity on binary memoryless symmetric (BMS) channels has drawn attention since it was resolved positively for the binary erasure channel by Kudekar et al. in 2016. In 2021, Reeves and Pfister extended this to prove the bit-error probability vanishes on BMS channe... | {
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2502.03787 | Iterate to Accelerate: A Unified Framework for Iterative Reasoning and
Feedback Convergence | [
"cs.LG"
] | We introduce a unified framework for iterative reasoning that leverages non-Euclidean geometry via Bregman divergences, higher-order operator averaging, and adaptive feedback mechanisms. Our analysis establishes that, under mild smoothness and contractivity assumptions, a generalized update scheme not only unifies clas... | {
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2502.03792 | Guiding Two-Layer Neural Network Lipschitzness via Gradient Descent
Learning Rate Constraints | [
"stat.ML",
"cs.LG"
] | We demonstrate that applying an eventual decay to the learning rate (LR) in empirical risk minimization (ERM), where the mean-squared-error loss is minimized using standard gradient descent (GD) for training a two-layer neural network with Lipschitz activation functions, ensures that the resulting network exhibits a hi... | {
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2502.03793 | It's All in The [MASK]: Simple Instruction-Tuning Enables BERT-like
Masked Language Models As Generative Classifiers | [
"cs.CL",
"cs.AI"
] | While encoder-only models such as BERT and ModernBERT are ubiquitous in real-world NLP applications, their conventional reliance on task-specific classification heads can limit their applicability compared to decoder-based large language models (LLMs). In this work, we introduce ModernBERT-Large-Instruct, a 0.4B-parame... | {
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2502.03795 | Distribution learning via neural differential equations: minimal energy
regularization and approximation theory | [
"cs.LG",
"math.CA",
"stat.ME",
"stat.ML"
] | Neural ordinary differential equations (ODEs) provide expressive representations of invertible transport maps that can be used to approximate complex probability distributions, e.g., for generative modeling, density estimation, and Bayesian inference. We show that for a large class of transport maps $T$, there exists a... | {
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2502.03798 | Network-Wide Traffic Flow Estimation Across Multiple Cities with Global
Open Multi-Source Data: A Large-Scale Case Study in Europe and North America | [
"cs.LG"
] | Network-wide traffic flow, which captures dynamic traffic volume on each link of a general network, is fundamental to smart mobility applications. However, the observed traffic flow from sensors is usually limited across the entire network due to the associated high installation and maintenance costs. To address this i... | {
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2502.03799 | Enhancing Hallucination Detection through Noise Injection | [
"cs.CL",
"cs.SY",
"eess.SY"
] | Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations. Effectively detecting hallucinations is therefore crucial for the safe deployment of LLMs. Recent research has linked hallucinations to model uncertainty, suggesting that hallucinations can be detected by me... | {
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2502.03801 | SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Federated learning (FL) enables collaborative model training while preserving data privacy, but its decentralized nature exposes it to client-side data poisoning attacks (DPAs) and model poisoning attacks (MPAs) that degrade global model performance. While numerous proposed defenses claim substantial effectiveness, the... | {
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2502.03802 | MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in
Dynamical Systems | [
"cs.LG",
"math.DS",
"stat.ME"
] | Convergent Cross Mapping (CCM) is a powerful method for detecting causality in coupled nonlinear dynamical systems, providing a model-free approach to capture dynamic causal interactions. Partial Cross Mapping (PCM) was introduced as an extension of CCM to address indirect causality in three-variable systems by compari... | {
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2502.03803 | Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced
Data | [
"cs.LG"
] | This study presents a hierarchical mining framework for high-dimensional imbalanced data, leveraging a depth graph model to address the inherent performance limitations of conventional approaches in handling complex, high-dimensional data distributions with imbalanced sample representations. By constructing a structure... | {
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2502.03804 | Understanding and Supporting Formal Email Exchange by Answering
AI-Generated Questions | [
"cs.HC",
"cs.AI"
] | Replying to formal emails is time-consuming and cognitively demanding, as it requires crafting polite phrasing and providing an adequate response to the sender's demands. Although systems with Large Language Models (LLMs) were designed to simplify the email replying process, users still need to provide detailed prompts... | {
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2502.03805 | Identify Critical KV Cache in LLM Inference from an Output Perturbation
Perspective | [
"cs.CL"
] | Large language models have revolutionized natural language processing but face significant challenges of high storage and runtime costs, due to the transformer architecture's reliance on self-attention, particularly the large Key-Value (KV) cache for long-sequence inference. Recent efforts to reduce KV cache size by pr... | {
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2502.03806 | Should Code Models Learn Pedagogically? A Preliminary Evaluation of
Curriculum Learning for Real-World Software Engineering Tasks | [
"cs.SE",
"cs.LG"
] | Learning-based techniques, especially advanced pre-trained models for code have demonstrated capabilities in code understanding and generation, solving diverse software engineering (SE) tasks. Despite the promising results, current training approaches may not fully optimize model performance, as they typically involve ... | {
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2502.03810 | DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models | [
"cs.CV"
] | Diffusion models have achieved significant progress in image generation. The pre-trained Stable Diffusion (SD) models are helpful for image deblurring by providing clear image priors. However, directly using a blurry image or pre-deblurred one as a conditional control for SD will either hinder accurate structure extrac... | {
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2502.03813 | Optimized Unet with Attention Mechanism for Multi-Scale Semantic
Segmentation | [
"cs.CV"
] | Semantic segmentation is one of the core tasks in the field of computer vision, and its goal is to accurately classify each pixel in an image. The traditional Unet model achieves efficient feature extraction and fusion through an encoder-decoder structure, but it still has certain limitations when dealing with complex ... | {
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2502.03814 | Large Language Models for Multi-Robot Systems: A Survey | [
"cs.RO",
"cs.AI"
] | The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, task planning, and human-robot interaction. Unlike traditional single-robot and multi-agent systems, MRS poses unique challenges, including coordination, scalability, and rea... | {
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2502.03817 | Knowing When to Stop Matters: A Unified Algorithm for Online Conversion
under Horizon Uncertainty | [
"cs.DS",
"cs.LG"
] | This paper investigates the online conversion problem, which involves sequentially trading a divisible resource (e.g., energy) under dynamically changing prices to maximize profit. A key challenge in online conversion is managing decisions under horizon uncertainty, where the duration of trading is either known, reveal... | {
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2502.03821 | PsyPlay: Personality-Infused Role-Playing Conversational Agents | [
"cs.CL"
] | The current research on Role-Playing Conversational Agents (RPCAs) with Large Language Models (LLMs) primarily focuses on imitating specific speaking styles and utilizing character backgrounds, neglecting the depiction of deeper personality traits.~In this study, we introduce personality-infused role-playing for LLM ag... | {
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2502.03822 | Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible
Training | [
"cs.RO"
] | Diffusion policies trained via offline behavioral cloning have recently gained traction in robotic motion generation. While effective, these policies typically require a large number of trainable parameters. This model size affords powerful representations but also incurs high computational cost during training. Ideall... | {
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2502.03824 | Syntriever: How to Train Your Retriever with Synthetic Data from LLMs | [
"cs.CL",
"cs.AI"
] | LLMs have boosted progress in many AI applications. Recently, there were attempts to distill the vast knowledge of LLMs into information retrieval systems. Those distillation methods mostly use output probabilities of LLMs which are unavailable in the latest black-box LLMs. We propose Syntriever, a training framework f... | {
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2502.03825 | Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net
Brain Tumor Segmentation | [
"eess.IV",
"cs.CR",
"cs.CV"
] | Deep learning-based medical image segmentation models, such as U-Net, rely on high-quality annotated datasets to achieve accurate predictions. However, the increasing use of generative models for synthetic data augmentation introduces potential risks, particularly in the absence of rigorous quality control. In this pap... | {
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2502.03826 | FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large
Language Model-Assisted Detection and Attribute Rebalancing | [
"cs.CV"
] | The proliferation of Text-to-Image (T2I) models has revolutionized content creation, providing powerful tools for diverse applications ranging from artistic expression to educational material development and marketing. Despite these technological advancements, significant ethical concerns arise from these models' relia... | {
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2502.03827 | A comprehensive survey of contemporary Arabic sentiment analysis:
Methods, Challenges, and Future Directions | [
"cs.CL",
"cs.AI"
] | Sentiment Analysis, a popular subtask of Natural Language Processing, employs computational methods to extract sentiment, opinions, and other subjective aspects from linguistic data. Given its crucial role in understanding human sentiment, research in sentiment analysis has witnessed significant growth in the recent ye... | {
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2502.03829 | FE-UNet: Frequency Domain Enhanced U-Net with Segment Anything
Capability for Versatile Image Segmentation | [
"cs.CV"
] | Image segmentation is a critical task in visual understanding. Convolutional Neural Networks (CNNs) are predisposed to capture high-frequency features in images, while Transformers exhibit a contrasting focus on low-frequency features. In this paper, we experimentally quantify the contrast sensitivity function of CNNs ... | {
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2502.03835 | Single-Domain Generalized Object Detection by Balancing Domain Diversity
and Invariance | [
"cs.CV"
] | Single-domain generalization for object detection (S-DGOD) aims to transfer knowledge from a single source domain to unseen target domains. In recent years, many models have focused primarily on achieving feature invariance to enhance robustness. However, due to the inherent diversity across domains, an excessive empha... | {
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2502.03836 | Adapting Human Mesh Recovery with Vision-Language Feedback | [
"cs.CV"
] | Human mesh recovery can be approached using either regression-based or optimization-based methods. Regression models achieve high pose accuracy but struggle with model-to-image alignment due to the lack of explicit 2D-3D correspondences. In contrast, optimization-based methods align 3D models to 2D observations but are... | {
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2502.03839 | On the Number of Control Nodes in Boolean Networks with Degree
Constraints | [
"eess.SY",
"cs.SY"
] | This paper studies the minimum control node set problem for Boolean networks (BNs) with degree constraints. The main contribution is to derive the nontrivial lower and upper bounds on the size of the minimum control node set through combinatorial analysis of four types of BNs (i.e., $k$-$k$-XOR-BNs, simple $k$-$k$-AND-... | {
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2502.03843 | Improving Natural Language Understanding for LLMs via Large-Scale
Instruction Synthesis | [
"cs.CL",
"cs.AI"
] | High-quality, large-scale instructions are crucial for aligning large language models (LLMs), however, there is a severe shortage of instruction in the field of natural language understanding (NLU). Previous works on constructing NLU instructions mainly focus on information extraction (IE), neglecting tasks such as mac... | {
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2502.03845 | PAGNet: Pluggable Adaptive Generative Networks for Information
Completion in Multi-Agent Communication | [
"cs.MA"
] | For partially observable cooperative tasks, multi-agent systems must develop effective communication and understand the interplay among agents in order to achieve cooperative goals. However, existing multi-agent reinforcement learning (MARL) with communication methods lack evaluation metrics for information weights and... | {
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2502.03850 | Electromagnetic Channel Modeling and Capacity Analysis for HMIMO
Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of t... | {
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2502.03852 | Pursuing Better Decision Boundaries for Long-Tailed Object Detection via
Category Information Amount | [
"cs.CV",
"cs.AI"
] | In object detection, the instance count is typically used to define whether a dataset exhibits a long-tail distribution, implicitly assuming that models will underperform on categories with fewer instances. This assumption has led to extensive research on category bias in datasets with imbalanced instance counts. Howev... | {
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} |
2502.03854 | Mirror Descent Actor Critic via Bounded Advantage Learning | [
"cs.LG"
] | Regularization is a core component of recent Reinforcement Learning (RL) algorithms. Mirror Descent Value Iteration (MDVI) uses both Kullback-Leibler divergence and entropy as regularizers in its value and policy updates. Despite its empirical success in discrete action domains and strong theoretical guarantees, the pe... | {
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} |
2502.03855 | Semi-rPPG: Semi-Supervised Remote Physiological Measurement with
Curriculum Pseudo-Labeling | [
"cs.CV"
] | Remote Photoplethysmography (rPPG) is a promising technique to monitor physiological signals such as heart rate from facial videos. However, the labeled facial videos in this research are challenging to collect. Current rPPG research is mainly based on several small public datasets collected in simple environments, whi... | {
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} |
2502.03856 | Taking A Closer Look at Interacting Objects: Interaction-Aware Open
Vocabulary Scene Graph Generation | [
"cs.CV"
] | Today's open vocabulary scene graph generation (OVSGG) extends traditional SGG by recognizing novel objects and relationships beyond predefined categories, leveraging the knowledge from pre-trained large-scale models. Most existing methods adopt a two-stage pipeline: weakly supervised pre-training with image captions a... | {
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} |
2502.03859 | Stabilizing scheduling logic for networked control systems under limited
capacity and lossy communication networks | [
"eess.SY",
"cs.SY"
] | In this paper we address the problem of designing scheduling logic for stabilizing Networked Control Systems (NCSs) with plants and controllers remotely-located over a limited capacity communication network subject to data losses. Our specific contributions include characterization of stability under worst case data lo... | {
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} |
2502.03860 | BOLT: Bootstrap Long Chain-of-Thought in Language Models without
Distillation | [
"cs.CL"
] | Large language models (LLMs), such as o1 from OpenAI, have demonstrated remarkable reasoning capabilities. o1 generates a long chain-of-thought (LongCoT) before answering a question. LongCoT allows LLMs to analyze problems, devise plans, reflect, and backtrack effectively. These actions empower LLM to solve complex pro... | {
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} |
2502.03866 | Weyl symmetry of the gradient-flow in information geometry | [
"gr-qc",
"cs.IT",
"math-ph",
"math.IT",
"math.MP"
] | We have revisited the gradient-flow in information geometry from the perspective of Weyl symmetry. The gradient-flow equations are derived from the proposed action which is invariant under the Weyl's gauge transformations. In Weyl integrable geometry, we have related Amari's $\alpha$-connections in IG to the Weyl invar... | {
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} |
2502.03876 | Position: Untrained Machine Learning for Anomaly Detection | [
"cs.LG"
] | Anomaly detection based on 3D point cloud data is an important research problem and receives more and more attention recently. Untrained anomaly detection based on only one sample is an emerging research problem motivated by real manufacturing industries such as personalized manufacturing that only one sample can be co... | {
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} |
2502.03877 | Advanced Object Detection and Pose Estimation with Hybrid Task Cascade
and High-Resolution Networks | [
"cs.CV"
] | In the field of computer vision, 6D object detection and pose estimation are critical for applications such as robotics, augmented reality, and autonomous driving. Traditional methods often struggle with achieving high accuracy in both object detection and precise pose estimation simultaneously. This study proposes an ... | {
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} |
2502.03884 | Rank Also Matters: Hierarchical Configuration for Mixture of Adapter
Experts in LLM Fine-Tuning | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs) have demonstrated remarkable success across various tasks, accompanied by a continuous increase in their parameter size. Parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), address the challenges of fine-tuning LLMs by significantly reducing the number of tra... | {
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} |
2502.03885 | InfinitePOD: Building Datacenter-Scale High-Bandwidth Domain for LLM
with Optical Circuit Switching Transceivers | [
"cs.NI",
"cs.DC",
"cs.LG"
] | Scaling Large Language Model (LLM) training relies on multi-dimensional parallelism, where High-Bandwidth Domains (HBDs) are critical for communication-intensive parallelism like Tensor Parallelism (TP) and Expert Parallelism (EP). However, existing HBD architectures face fundamental limitations in scalability, cost, a... | {
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} |
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