{"total":32,"rows":[{"id":"2207.08799","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","created":"2022-07-18","cited_by_count":187,"area":null},{"id":"2206.15144","title":"Neural Networks can Learn Representations with Gradient Descent","created":"2022-06-30","cited_by_count":161,"area":null},{"id":"2109.14545","title":"Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark","created":"2021-09-29","cited_by_count":155,"area":null},{"id":"2210.15651","title":"Learning Single-Index Models with Shallow Neural Networks","created":"2022-10-27","cited_by_count":92,"area":null},{"id":"2102.04401","title":"The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals","created":"2021-02-08","cited_by_count":52,"area":null},{"id":"2309.03843","title":"Gradient-Based Feature Learning under Structured Data","created":"2023-09-07","cited_by_count":32,"area":null},{"id":"2302.06512","title":"Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals","created":"2023-02-13","cited_by_count":31,"area":null},{"id":"2202.05258","title":"Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks","created":"2022-02-10","cited_by_count":28,"area":null},{"id":"2101.08303","title":"From Local Pseudorandom Generators to Hardness of Learning","created":"2021-01-20","cited_by_count":24,"area":null},{"id":"2106.13792","title":"Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent","created":"2021-06-25","cited_by_count":21,"area":null},{"id":"2106.10744","title":"On the Cryptographic Hardness of Learning Single Periodic Neurons","created":"2021-06-20","cited_by_count":20,"area":null},{"id":"2206.08918","title":"Learning a Single Neuron with Adversarial Label Noise via Gradient Descent","created":"2022-06-17","cited_by_count":20,"area":null},{"id":"2102.08668","title":"Non-asymptotic approximations of neural networks by Gaussian processes","created":"2021-02-17","cited_by_count":19,"area":null},{"id":"2101.01152","title":"Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise","created":"2021-01-04","cited_by_count":17,"area":null},{"id":"2106.01101","title":"Learning a Single Neuron with Bias Using Gradient Descent","created":"2021-06-02","cited_by_count":17,"area":null},{"id":"2306.10615","title":"Agnostically Learning Single-Index Models using Omnipredictors","created":"2023-06-18","cited_by_count":16,"area":null},{"id":"2307.06887","title":"Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks","created":"2023-07-13","cited_by_count":16,"area":null},{"id":"2202.08064","title":"Learning a Single Neuron for Non-monotonic Activation Functions","created":"2022-02-16","cited_by_count":13,"area":null},{"id":"2210.13601","title":"Active Learning for Single Neuron Models with Lipschitz Non-Linearities","created":"2022-10-24","cited_by_count":12,"area":null},{"id":"2306.07892","title":"Robustly Learning a Single Neuron via Sharpness","created":"2023-06-13","cited_by_count":12,"area":null},{"id":"2109.04623","title":"ReLU Regression with Massart Noise","created":"2021-09-10","cited_by_count":10,"area":null},{"id":"2302.07426","title":"Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy","created":"2023-02-15","cited_by_count":10,"area":null},{"id":"2310.08425","title":"Differentially Private Non-convex Learning for Multi-layer Neural Networks","created":"2023-10-12","cited_by_count":10,"area":null},{"id":"2303.02255","title":"Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron","created":"2023-03-03","cited_by_count":8,"area":null},{"id":"2306.16932","title":"A Quantitative Functional Central Limit Theorem for Shallow Neural Networks","created":"2023-06-29","cited_by_count":8,"area":null},{"id":"2411.14349","title":"Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals","created":"2024-11-21","cited_by_count":5,"area":null},{"id":"2202.00834","title":"Nonlinear Initialization Methods for Low-Rank Neural Networks","created":"2022-02-02","cited_by_count":3,"area":null},{"id":"2411.06697","title":"Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise","created":"2024-11-11","cited_by_count":2,"area":null},{"id":"2309.11657","title":"Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions","created":"2023-09-20","cited_by_count":1,"area":null},{"id":"2111.01166","title":"Dynamics of Local Elasticity During Training of Neural Nets","created":"2021-11-01","cited_by_count":0,"area":null},{"id":"2505.05819","title":"New Statistical and Computational Results for Learning Junta Distributions","created":"2025-05-09","cited_by_count":0,"area":null},{"id":"2601.18115","title":"Robust Learning of a Group DRO Neuron","created":"2026-01-26","cited_by_count":0,"area":null}],"next_cursor":null}