{"total":67,"rows":[{"id":"2205.09653","title":"Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks","created":"2022-05-19","cited_by_count":105,"area":null},{"id":"2305.16380","title":"Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer","created":"2023-05-25","cited_by_count":102,"area":null},{"id":"2310.00535","title":"JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention","created":"2023-10-01","cited_by_count":52,"area":null},{"id":"2102.02410","title":"A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network","created":"2021-02-04","cited_by_count":47,"area":null},{"id":"2106.03795","title":"Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks","created":"2021-06-07","cited_by_count":44,"area":null},{"id":"2305.18411","title":"Feature-Learning Networks Are Consistent Across Widths At Realistic Scales","created":"2023-05-28","cited_by_count":41,"area":null},{"id":"2206.10012","title":"Limitations of the NTK for Understanding Generalization in Deep Learning","created":"2022-06-20","cited_by_count":35,"area":null},{"id":"2105.14301","title":"A Theory of Neural Tangent Kernel Alignment and Its Influence on Training","created":"2021-05-29","cited_by_count":25,"area":null},{"id":"2203.07379","title":"Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks","created":"2022-03-14","cited_by_count":25,"area":null},{"id":"2302.10034","title":"Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron","created":"2023-02-20","cited_by_count":25,"area":null},{"id":"2310.19103","title":"Proving Linear Mode Connectivity of Neural Networks via Optimal Transport","created":"2023-10-29","cited_by_count":25,"area":null},{"id":"2105.14417","title":"Overparameterization of deep ResNet: zero loss and mean-field analysis","created":"2021-05-30","cited_by_count":24,"area":null},{"id":"2212.03050","title":"Uniform-in-time propagation of chaos for mean field Langevin dynamics","created":"2022-12-06","cited_by_count":23,"area":null},{"id":"2106.06573","title":"Understanding Deflation Process in Over-parametrized Tensor Decomposition","created":"2021-06-11","cited_by_count":20,"area":null},{"id":"2308.13431","title":"Six Lectures on Linearized Neural Networks","created":"2023-08-25","cited_by_count":20,"area":null},{"id":"2401.07187","title":"A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models","created":"2024-01-14","cited_by_count":20,"area":null},{"id":"2305.18512","title":"A Rainbow in Deep Network Black Boxes","created":"2023-05-29","cited_by_count":18,"area":null},{"id":"2310.01769","title":"How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization","created":"2023-10-03","cited_by_count":18,"area":null},{"id":"2105.05228","title":"Global Convergence of Three-layer Neural Networks in the Mean Field Regime","created":"2021-05-11","cited_by_count":16,"area":null},{"id":"2110.08084","title":"Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization","created":"2021-10-15","cited_by_count":16,"area":null},{"id":"2108.13097","title":"A theory of representation learning gives a deep generalisation of kernel methods","created":"2021-08-30","cited_by_count":15,"area":null},{"id":"2102.08373","title":"Analysis of feature learning in weight-tied autoencoders via the mean field lens","created":"2021-02-16","cited_by_count":14,"area":null},{"id":"2111.02278","title":"Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks","created":"2021-11-03","cited_by_count":13,"area":null},{"id":"2204.10782","title":"On Feature Learning in Neural Networks with Global Convergence Guarantees","created":"2022-04-22","cited_by_count":13,"area":null},{"id":"2307.05635","title":"Fundamental limits of overparametrized shallow neural networks for supervised learning","created":"2023-07-11","cited_by_count":13,"area":null},{"id":"2312.11737","title":"Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes","created":"2023-12-18","cited_by_count":13,"area":null},{"id":"2110.02926","title":"On the Global Convergence of Gradient Descent for multi-layer ResNets in the mean-field regime","created":"2021-10-06","cited_by_count":12,"area":null},{"id":"2202.02981","title":"Neural Tangent Kernel Analysis of Deep Narrow Neural Networks","created":"2022-02-07","cited_by_count":11,"area":null},{"id":"2304.01063","title":"Depth Separation with Multilayer Mean-Field Networks","created":"2023-04-03","cited_by_count":9,"area":null},{"id":"2305.07810","title":"Depth Dependence of $\\mu$P Learning Rates in ReLU MLPs","created":"2023-05-13","cited_by_count":9,"area":null},{"id":"2411.07536","title":"Model Stealing for Any Low-Rank Language Model","created":"2024-11-12","cited_by_count":9,"area":null},{"id":"2211.08771","title":"On the symmetries in the dynamics of wide two-layer neural networks","created":"2022-11-16","cited_by_count":8,"area":null},{"id":"2403.12887","title":"Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport","created":"2024-03-19","cited_by_count":8,"area":null},{"id":"2110.15596","title":"Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit","created":"2021-10-29","cited_by_count":7,"area":null},{"id":"2505.03577","title":"Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime","created":"2025-05-06","cited_by_count":6,"area":null},{"id":"2601.19756","title":"Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining","created":"2026-01-27","cited_by_count":6,"area":null},{"id":"2110.15954","title":"Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training","created":"2021-10-29","cited_by_count":5,"area":null},{"id":"2210.11452","title":"Global Convergence of SGD On Two Layer Neural Nets","created":"2022-10-20","cited_by_count":5,"area":null},{"id":"2307.01177","title":"Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space","created":"2023-07-03","cited_by_count":5,"area":null},{"id":"2503.09565","title":"Global Convergence and Rich Feature Learning in $L$-Layer Infinite-Width Neural Networks under $\\mu$P Parametrization","created":"2025-03-12","cited_by_count":4,"area":null},{"id":"2111.09459","title":"Gradient flows on graphons: existence, convergence, continuity equations","created":"2021-11-18","cited_by_count":3,"area":null},{"id":"2205.09860","title":"Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates","created":"2022-05-19","cited_by_count":3,"area":null},{"id":"2210.06819","title":"Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence","created":"2022-10-13","cited_by_count":3,"area":null},{"id":"2210.16286","title":"A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks","created":"2022-10-28","cited_by_count":3,"area":null},{"id":"2309.09258","title":"Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets","created":"2023-09-17","cited_by_count":3,"area":null},{"id":"2309.16923","title":"Mode Connectivity and Data Heterogeneity of Federated Learning","created":"2023-09-29","cited_by_count":3,"area":null},{"id":"2604.27077","title":"Learning Rate Transfer in Normalized Transformers","created":"2026-04-29","cited_by_count":3,"area":null},{"id":"2303.00970","title":"PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium","created":"2023-03-02","cited_by_count":2,"area":null},{"id":"2412.01619","title":"Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics","created":"2024-12-02","cited_by_count":2,"area":null},{"id":"2301.09269","title":"M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion","created":"2023-01-23","cited_by_count":1,"area":null},{"id":"2303.06726","title":"Global Optimality of Elman-type RNN in the Mean-Field Regime","created":"2023-03-12","cited_by_count":1,"area":null},{"id":"2310.01683","title":"Commutative Width and Depth Scaling in Deep Neural Networks","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2405.11451","title":"Error Analysis of Three-Layer Neural Network Trained with PGD for Deep Ritz Method","created":"2024-05-19","cited_by_count":1,"area":null},{"id":"2511.21466","title":"Mean-Field Model for Two-Layer Neural Networks Trained with Consensus-Based Optimization","created":"2025-11-26","cited_by_count":1,"area":null},{"id":"2512.17593","title":"A Unified Representation of Neural Networks Architectures","created":"2025-12-19","cited_by_count":1,"area":null},{"id":"2602.04548","title":"Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions","created":"2026-02-04","cited_by_count":1,"area":null},{"id":"2605.10775","title":"On the global convergence of gradient flow for wide shallow models beyond homogeneous nonlinearities","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2201.07934","title":"Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks","created":"2022-01-03","cited_by_count":0,"area":null},{"id":"2204.08211","title":"How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct","created":"2022-04-18","cited_by_count":0,"area":null},{"id":"2206.03254","title":"Demystifying the Global Convergence Puzzle of Learning Over-parameterized ReLU Nets in Very High Dimensions","created":"2022-06-05","cited_by_count":0,"area":null},{"id":"2309.06774","title":"Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss","created":"2023-09-13","cited_by_count":0,"area":null},{"id":"2408.08681","title":"A Mean Field Ansatz for Zero-Shot Weight Transfer","created":"2024-08-16","cited_by_count":0,"area":null},{"id":"2503.23016","title":"Towards Understanding the Optimization Mechanisms in Deep Learning","created":"2025-03-29","cited_by_count":0,"area":null},{"id":"2504.08489","title":"Statistically guided deep learning","created":"2025-04-11","cited_by_count":0,"area":null},{"id":"2510.22667","title":"Block Coordinate Descent for Neural Networks Provably Finds Global Minima","created":"2025-10-26","cited_by_count":0,"area":null},{"id":"2512.21075","title":"Feature Learning Dynamics in Infinite-Depth Neural Networks","created":"2025-12-24","cited_by_count":0,"area":null},{"id":"2607.05735","title":"Width-Robust Learnability in Mean-Field Bayesian Neural Networks","created":"2026-07-07","cited_by_count":0,"area":null}],"next_cursor":null}