On the Interaction of Batch Noise, Adaptivity, and Compression, under $(L_0,L_1)$-Smoothness: An SDE Approach - 惯性聚合

推荐订阅源

Help Net Security

Future of Privacy Forum

The GitHub Blog

博客园 - 叶小钗

阮一峰的网络日志

Kaspersky official blog

Google Developers Blog

freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

Privacy International News Feed

Cyber Attacks, Cyber Crime and Cyber Security

News | PayPal Newsroom

Schneier on Security

Microsoft Azure Blog

The Hacker News

Stack Overflow Blog

Security Latest

Microsoft Research Blog - Microsoft Research

Google Online Security Blog

博客园_首页

CXSECURITY Database RSS Feed - CXSecurity.com

Google DeepMind News

Y Combinator Blog

The Cloudflare Blog

Microsoft Security Blog

Cisco Talos Blog

钛媒体：引领未来商业与生活新知

Troy Hunt's Blog

Fox-IT International blog

Security @ Cisco Blogs

博客园 - 司徒正美

cs.CV updates on arXiv.org

Comments on: Blog

Threat Intelligence Blog | Flashpoint

LINUX DO - 最新话题

Tailwind CSS Blog

cs.LG updates on arXiv.org

Learning Laplacian Eigenspace with Mass-Aware Neural Operators on Point Clouds Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning Eureka: Intelligent Feature Engineering for Enterprise AI Cloud Resource Demand Prediction An Effective-Rank Audit of Alignment-Induced Activation Shifts: Confound Control, Constructive Calibration, and Limits LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots Large Language Model Selection with Limited Annotations Assessing the Operational Viability of Foundation Models for Time Series Forecasting CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning Spectral Probe-Circuits: A Three-Step Recipe for Identifying Attention-Head Circuits in Pretrained Transformers Lake Detection and Water Quality Estimation in Sentinel-2 Data Faithfulness as Information Flow: Evaluating and Training Faithful Chain-of-Thought Reasoning A lift for input-convex neural network training LLM-AutoSciLab: Closed-Loop Scientific Discovery via Active Experimentation with LLMs Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems CSP-Atlas: Concept-Specific Neural Circuits in a Sparse Python Transformer Beyond Fixed Points: Superpolynomial Capacity of Asymmetric Hopfield Networks AvAtar: Learning to Align via Active Optimal Transport Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette IterInject: Indirect Prompt Injection Against LLM Agents via Feedback-Guided Iterative Optimization Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection Omissive Bias in Religious Representation: Benchmarking LLM Answers to Everyday Ethical Decision-making Towards Verifiable Transformers: Solver-Checkable Circuit Explanations Generative OOD-regularized Model-based Policy Optimization Synheart Capacity: A Theory-Driven Physiological Representation of Cognitive Capacity Dynamics from Wearable Signals PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets Extracting Training Data from Diffusion Language Models via Infilling Filtered Posterior Mean Collections: A Unified Framework for Analytical Models of Diffusion Generalization Cascade-KDE: Robust Time-Series Restoration under Out-of-Distribution Impulse Corruptions When Reasoning Hurts: Source-Aware Evaluation of Frontier LLMs for Clinical SOAP Note Generation Feature Learning in Wide Neural Networks under $μ$P: Identifiability and Sparse-Dictionary Decomposition of the Mean-Field Limit A computational phase transition for learning-to-sample from Ising models Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants Mixture of Complementary Agents for Robust LLM Ensemble Rethinking Federated Unlearning via the Lens of Memorization CAFD: Concept-Aware DNN Fault Detection using VLMs ECHO: Terminal Agents Learn World Models for Free Discovering Lexical Gaps Using Embeddings from Multilingual LLMs Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting SemanticZip: A Pilot Framework for Lossy Text Compression with LLMs as Semantic Decompressors Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing LAPLEX: The FFT of Learnable Laplace Kernels Refined Analysis of Entropy-Regularized Actor-Critic Towards a Universal Causal Reasoner Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference Polymorphism Is Rotation: Operational Mechanistic Interpretability from a Two-Layer Transformer to Pythia-70m Hardware-Aware Federated Learning for Speech Emotion Recognition Measuring the Depth of LLM Unlearning via Activation Patching Fourier Feature Pyramids for Physics-Informed Neural Networks BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation Agent-ToM: Learning to Monitor Autonomous LLM Agents via Theory-of-Mind Reasoning Treatment Effect Estimation with Differentiated Networked Effect on Graph Data WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations Private Adaptive Covariance Estimation via Gaussian Graphical Models Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions Algometrics: Forecasting Under Algorithmic Feedback ChaosBench-Logic v2: Evaluating LLM Logical Reasoning over Dynamical Systems at Scale TRACE: A taxonomy-grounded synthetic dataset for teaching-program generation and session interpretation in Applied Behavior Analysis TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models Not All Transitions Matter: Evidence from PPO From One-Pass SGD to Data Reuse: Mini-Batch Scaling Laws in Sketched Linear Regression GEESE: Genotype-aware End-to-End Spatio-temporal Embedding for Behavioral Phenotyping ChainLearn: A Blockchain-Based Capacity-Aware Framework for Federated Ensemble Learning A general tensor-structured compression scheme for efficient large language models What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training The Normalized Maximum Likelihood for Regular Non-Smooth Models: Measure-Theoretic Foundations and Geometric Sampling Momentum Streams for Optimizer-Inspired Transformers Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions RL with Learnable Textual Feedback: A Bilevel Approach Representation-Guided Discrete Molecular Graph Retrosynthesis Trajectory-Based Difficulty Scoring for Reliable Learning on Tabular Data Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood? Batch Normalization Amplifies Memorization and Privacy Risks Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions Signs Beat Floats: Low-Rank Double-Binary Adaptation for On-Device Fine-Tuning Interdomain Attention: Beyond Token-Level Key-Value Memory Characterizing the Representational Capacity of Neural Processes Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs LLMs Show No Signs Of Individuated Metacognition PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection Optimizing Digital Therapeutic Interventions: Online Learning under Endogenous Adherence Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality Feature Lottery? A Bifurcation Theory of Concept Emergence Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents Hidden-State Privacy Has an Empty Middle A Unified Python Framework for Direct PPO-based Control of AHUs with Economizer Logic and CO2-Constrained Ventilation Beyond Generative Priors: Minority Sampling with JEPA-Guided Diffusion Zeroth-Order Nonconvex Nonsmooth Optimization with Heavy-Tailed Noise MindAlign: Bridging EEG, Vision, and Language for Zero-Shot Visual Decoding CAffNet: Hard Constraint-Affine Neural Networks Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization Knowledge Graph Modulated Deep Learning for Limited-Sample Clinical Data Analysis

On the Interaction of Batch Noise, Adaptivity, and Compression, under $(L_0,L_1)$-Smoothness: An SDE Approach

Enea Monzio · 2026-05-26 · via cs.LG updates on arXiv.org

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。原文来自 — 版权归原作者所有。