惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

爱范儿
爱范儿
B
Blog RSS Feed
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
量
量子位
博
博客园 - 三生石上(FineUI控件)
博
博客园 - 【当耐特】
Attack and Defense Labs
Attack and Defense Labs
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
人人都是产品经理
人人都是产品经理
酷 壳 – CoolShell
酷 壳 – CoolShell
Apple Machine Learning Research
Apple Machine Learning Research
阮一峰的网络日志
阮一峰的网络日志
大猫的无限游戏
大猫的无限游戏
T
Tailwind CSS Blog
钛媒体：引领未来商业与生活新知
钛媒体：引领未来商业与生活新知
罗
罗磊的独立博客
V
Visual Studio Blog
博
博客园 - Franky
博
博客园 - 叶小钗
有赞技术团队
有赞技术团队
IT之家
IT之家
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博
博客园_首页
J
Java Code Geeks
S
SegmentFault 最新的问题
Last Week in AI
Last Week in AI
月光博客
月光博客
博
博客园 - 司徒正美
小众软件
小众软件
The Cloudflare Blog
宝玉的分享
宝玉的分享
博
博客园 - 聂微东
WordPress大学
WordPress大学
雷峰网
雷峰网
V
V2EX
Engineering at Meta
Engineering at Meta
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
L
LangChain Blog
Jina AI
Jina AI
Hugging Face - Blog
Hugging Face - Blog
The Register - Security
The Register - Security
腾
腾讯CDC
Microsoft Azure Blog
Microsoft Azure Blog
Recent Announcements
Recent Announcements
D
Docker
F
Fortinet All Blogs
美
美团技术团队
H
Help Net Security
U
Unit 42
MyScale Blog
MyScale Blog

stat.ML updates on arXiv.org

Adaptive multi-fidelity optimization with fast learning rates Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance Collective Kernel EFT for Pre-activation ResNets PRIM-cipal components analysis One-Shot Generative Flows: Existence and Obstructions Structural interpretability in SVMs with truncated orthogonal polynomial kernels Amortized Optimal Transport from Sliced Potentials MinShap: A Modified Shapley Value Approach for Feature Selection Unsupervised feature selection using Bayesian Tucker decomposition Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits Best of both worlds: Stochastic & adversarial best-arm identification Scalable Model-Based Clustering with Sequential Monte Carlo Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness: An Application to Seismic Monitoring Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks Gating Enables Curvature: A Geometric Expressivity Gap in Attention Zeroth-Order Optimization at the Edge of Stability Differentially Private Conformal Prediction CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization Generative Augmented Inference Improving Machine Learning Performance with Synthetic Augmentation PAC-MCTS: Bias-Aware Pruning for Robust LLM-Guided Search and Planning Path-Sampled Integrated Gradients Heat and Matérn Kernels on Matchings Doubly Outlier-Robust Online Infinite Hidden Markov Model Momentum Further Constrains Sharpness at the Edge of Stochastic Stability Multistage Conditional Compositional Optimization BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization Sandpile Economics: Theory, Identification, and Evidence Online learning with noisy side observations Spectral Thompson sampling Covariance-adapting algorithm for semi-bandits with application to sparse rewards Ordinary Least Squares is a Special Case of Transformer Metric-Aware Principal Component Analysis (MAPCA):A Unified Framework for Scale-Invariant Representation Learning Robust Low-Rank Tensor Completion based on M-product with Weighted Correlated Total Variation and Sparse Regularization Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization Universality of Gaussian-Mixture Reverse Kernels in Conditional Diffusion Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity Some Theoretical Limitations of t-SNE Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing Rare Event Analysis via Stochastic Optimal Control Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates Minimizing classical resources in variational measurement-based quantum computation for generative modeling Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers ADD for Multi-Bit Image Watermarking Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables Regional Explanations: Bridging Local and Global Variable Importance ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values Cost-optimal Sequential Testing via Doubly Robust Q-learning Query Lower Bounds for Diffusion Sampling Tail-Aware Information-Theoretic Generalization for RLHF and SGLD Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer Hierarchical Kernel Transformer: Multi-Scale Attention with an Information-Theoretic Approximation Analysis Policy-Aware Design of Large-Scale Factorial Experiments Towards Verified and Targeted Explanations through Formal Methods Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training Spectral methods: crucial for machine learning, natural for quantum computers? The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches Certified and accurate computation of function space norms of deep neural networks Mini-Batch Covariance, Diffusion Limits, and Oracle Complexity in Stochastic Gradient Descent: A Sampling-Design Perspective Conformal Policy Control Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare Neural Networks With Dense Weights Are Not Universal Approximators Continuous-time reinforcement learning: ellipticity enables model-free value function approximation Scalable spatial point process models for forensic footwear analysis A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios Active Learning with Selective Time-Step Acquisition for PDEs Joint Score-Threshold Optimization for Interpretable Risk Assessment Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems Online Distributionally Robust LLM Alignment via Regression to Relative Reward Heavy-Tailed Class-Conditional Priors for Long-Tailed Generative Modeling Random Walk Learning and the Pac-Man Attack Sequential Regression Learning with Randomized Algorithms Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes Towards AI-assisted Neutrino Flavor Theory Design Towards Reasonable Concept Bottleneck Models Practical estimation of the optimal classification error with soft labels and calibration Flow-based Generative Modeling of Potential Outcomes and Counterfactuals The Gaussian Latent Machine: Efficient Prior and Posterior Sampling for Inverse Problems Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users Identifying Information from Observations with Uncertainty and Novelty A ghost mechanism: An analytical model of abrupt learning in recurrent networks A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis Large Language Models for Market Research: A Data-augmentation Approach Transformer Neural Processes - Kernel Regression FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening Estimating Joint Interventional Distributions from Marginal Interventional Data Nonparametric Sparse Online Learning of the Koopman Operator
On the linearity of large non-linear models: when and why the tangent kernel is constant
Chaoyue Liu, Libin Zhu, Mikhail Belkin · 2020-10-03 · via stat.ML updates on arXiv.org

The goal of this work is to shed light on the remarkable phenomenon of transition to linearity of certain neural networks as their width approaches infinity. We show that the transition to linearity of the model and, equivalently, constancy of the (neural) tangent kernel (NTK) result from the scaling properties of the norm of the Hessian matrix of the network as a function of the network width. We present a general framework for understanding the constancy of the tangent kernel via Hessian scaling applicable to the standard classes of neural networks. Our analysis provides a new perspective on the phenomenon of constant tangent kernel, which is different from the widely accepted "lazy training". Furthermore, we show that the transition to linearity is not a general property of wide neural networks and does not hold when the last layer of the network is non-linear. It is also not necessary for successful optimization by gradient descent.

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。