{"list":"other","total":51,"rows":[{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1806.07572","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","created":"2018-06-20","cited_by_count":2621},{"id":"1905.02175","title":"Adversarial Examples Are Not Bugs, They Are Features","created":"2019-05-06","cited_by_count":953},{"id":"1805.12152","title":"Robustness May Be at Odds with Accuracy","created":"2018-05-30","cited_by_count":880},{"id":"1706.08498","title":"Spectrally-normalized margin bounds for neural networks","created":"2017-06-26","cited_by_count":686},{"id":"1812.07956","title":"On Lazy Training in Differentiable Programming","created":"2018-12-19","cited_by_count":675},{"id":"1804.06561","title":"A Mean Field View of the Landscape of Two-Layers Neural Networks","created":"2018-04-18","cited_by_count":466},{"id":"1708.06633","title":"Nonparametric regression using deep neural networks with ReLU activation function","created":"2017-08-22","cited_by_count":433},{"id":"1805.09545","title":"On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport","created":"2018-05-24","cited_by_count":426},{"id":"1412.8690","title":"Breaking the Curse of Dimensionality with Convex Neural Networks","created":"2014-12-30","cited_by_count":331},{"id":"1804.11285","title":"Adversarially Robust Generalization Requires More Data","created":"2018-04-30","cited_by_count":318},{"id":"2002.04486","title":"Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss","created":"2020-02-11","cited_by_count":268},{"id":"2207.08799","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","created":"2022-07-18","cited_by_count":187},{"id":"2206.15144","title":"Neural Networks can Learn Representations with Gradient Descent","created":"2022-06-30","cited_by_count":161},{"id":"2205.01445","title":"High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation","created":"2022-05-03","cited_by_count":150},{"id":"2302.11055","title":"SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics","created":"2023-02-21","cited_by_count":143},{"id":"2202.08658","title":"The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks","created":"2022-02-17","cited_by_count":142},{"id":"1810.05369","title":"Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel","created":"2018-10-12","cited_by_count":131},{"id":"2003.10409","title":"Online stochastic gradient descent on non-convex losses from high-dimensional inference","created":"2020-03-23","cited_by_count":119},{"id":"2210.15651","title":"Learning Single-Index Models with Shallow Neural Networks","created":"2022-10-27","cited_by_count":92},{"id":"1805.10204","title":"Adversarial examples from computational constraints","created":"2018-05-25","cited_by_count":80},{"id":"2002.10477","title":"Precise Tradeoffs in Adversarial Training for Linear Regression","created":"2020-02-24","cited_by_count":77},{"id":"2209.14863","title":"Neural Networks Efficiently Learn Low-Dimensional Representations with SGD","created":"2022-09-29","cited_by_count":63},{"id":"2305.10633","title":"Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models","created":"2023-05-18","cited_by_count":61},{"id":"2201.10469","title":"Convex Analysis of the Mean Field Langevin Dynamics","created":"2022-01-25","cited_by_count":55},{"id":"2402.03220","title":"The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents","created":"2024-02-05","cited_by_count":52},{"id":"2010.11213","title":"Precise Statistical Analysis of Classification Accuracies for Adversarial Training","created":"2020-10-21","cited_by_count":50},{"id":"2310.07891","title":"A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks","created":"2023-10-11","cited_by_count":49},{"id":"2406.01581","title":"Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit","created":"2024-06-03","cited_by_count":46},{"id":"2201.05149","title":"The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression","created":"2022-01-13","cited_by_count":41},{"id":"2309.15111","title":"SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem","created":"2023-09-26","cited_by_count":39},{"id":"2405.15459","title":"Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions","created":"2024-05-24","cited_by_count":37},{"id":"2004.13748","title":"Learning Polynomials of Few Relevant Dimensions","created":"2020-04-28","cited_by_count":34},{"id":"2309.03843","title":"Gradient-Based Feature Learning under Structured Data","created":"2023-09-07","cited_by_count":32},{"id":"2204.02735","title":"Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck","created":"2022-04-06","cited_by_count":30},{"id":"2105.08368","title":"Convergence Rates of Gradient Methods for Convex Optimization in the Space of Measures","created":"2021-05-18","cited_by_count":20},{"id":"2306.16361","title":"Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time","created":"2023-06-28","cited_by_count":20},{"id":"2305.06986","title":"Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks","created":"2023-05-11","cited_by_count":19},{"id":"2307.15804","title":"On Single Index Models beyond Gaussian Data","created":"2023-07-28","cited_by_count":17},{"id":"2307.06887","title":"Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks","created":"2023-07-13","cited_by_count":16},{"id":"2407.05622","title":"On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries","created":"2024-07-08","cited_by_count":15},{"id":"2408.07254","title":"Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics","created":"2024-08-14","cited_by_count":15},{"id":"2406.11828","title":"Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations","created":"2024-06-17","cited_by_count":13},{"id":"2410.08503","title":"Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data","created":"2024-10-11","cited_by_count":12},{"id":"2311.13774","title":"Learning Hierarchical Polynomials with Three-Layer Neural Networks","created":"2023-11-23","cited_by_count":9},{"id":"2110.11950","title":"Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff","created":"2021-10-22","cited_by_count":8},{"id":"2312.15469","title":"Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products","created":"2023-12-24","cited_by_count":7},{"id":"2406.17054","title":"Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach","created":"2024-06-24","cited_by_count":6},{"id":"2406.08658","title":"Pruning is Optimal for Learning Sparse Features in High-Dimensions","created":"2024-06-12","cited_by_count":5},{"id":"2402.08948","title":"Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input","created":"2024-02-14","cited_by_count":1}],"next_cursor":null}