{"list":"other","total":54,"rows":[{"id":"1806.07572","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","created":"2018-06-20","cited_by_count":2621},{"id":"1902.06720","title":"Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent","created":"2019-02-18","cited_by_count":738},{"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":"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":"1908.05355","title":"The generalization error of random features regression: Precise asymptotics and double descent curve","created":"2019-08-14","cited_by_count":413},{"id":"1605.09782","title":"Adversarial Feature Learning","created":"2016-05-31","cited_by_count":350},{"id":"1810.01075","title":"Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning","created":"2018-10-02","cited_by_count":215},{"id":"2011.14522","title":"Feature Learning in Infinite-Width Neural Networks","created":"2020-11-30","cited_by_count":190},{"id":"1902.06015","title":"Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit","created":"2019-02-16","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":"1904.12191","title":"Linearized two-layers neural networks in high dimension","created":"2019-04-27","cited_by_count":155},{"id":"2006.13409","title":"When Do Neural Networks Outperform Kernel Methods?","created":"2020-06-24","cited_by_count":150},{"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":"2102.08127","title":"Learning curves of generic features maps for realistic datasets with a teacher-student model","created":"2021-02-16","cited_by_count":126},{"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":"2002.06716","title":"Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data","created":"2020-02-17","cited_by_count":115},{"id":"2006.14709","title":"The Gaussian equivalence of generative models for learning with shallow neural networks","created":"2020-06-25","cited_by_count":113},{"id":"1808.09372","title":"Mean Field Analysis of Neural Networks: A Central Limit Theorem","created":"2018-08-28","cited_by_count":107},{"id":"1805.00915","title":"Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach","created":"2018-05-02","cited_by_count":106},{"id":"2008.06786","title":"The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization","created":"2020-08-15","cited_by_count":101},{"id":"1909.08156","title":"Dynamics of Deep Neural Networks and Neural Tangent Hierarchy","created":"2019-09-18","cited_by_count":96},{"id":"1702.05419","title":"A Random Matrix Approach to Neural Networks","created":"2017-02-17","cited_by_count":80},{"id":"2011.03321","title":"Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition","created":"2020-11-04","cited_by_count":76},{"id":"2202.08832","title":"Universality of empirical risk minimization","created":"2022-02-17","cited_by_count":75},{"id":"2305.18270","title":"How Two-Layer Neural Networks Learn, One (Giant) Step at a Time","created":"2023-05-29","cited_by_count":68},{"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":"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":"1804.02605","title":"Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression","created":"2018-04-08","cited_by_count":42},{"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":"2402.04980","title":"Asymptotics of feature learning in two-layer networks after one gradient-step","created":"2024-02-07","cited_by_count":38},{"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":"2211.06506","title":"Spectral Evolution and Invariance in Linear-width Neural Networks","created":"2022-11-11","cited_by_count":30},{"id":"2010.05170","title":"What causes the test error? Going beyond bias-variance via ANOVA","created":"2020-10-11","cited_by_count":28},{"id":"1904.03090","title":"Eigenvalue distribution of nonlinear models of random matrices","created":"2019-04-05","cited_by_count":27},{"id":"2402.10127","title":"Nonlinear spiked covariance matrices and signal propagation in deep neural networks","created":"2024-02-15","cited_by_count":26},{"id":"2302.06210","title":"Precise Asymptotic Analysis of Deep Random Feature Models","created":"2023-02-13","cited_by_count":22},{"id":"1912.00827","title":"A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning","created":"2019-12-02","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":"2405.15480","title":"Fundamental computational limits of weak learnability in high-dimensional multi-index models","created":"2024-05-24","cited_by_count":19},{"id":"2210.12760","title":"On double-descent in uncertainty quantification in overparametrized models","created":"2022-10-23","cited_by_count":16},{"id":"2410.18938","title":"A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities","created":"2024-10-24","cited_by_count":16},{"id":"2503.02877","title":"Weak-to-Strong Generalization Even in Random Feature Networks, Provably","created":"2025-03-04","cited_by_count":15},{"id":"2302.01629","title":"Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels","created":"2023-02-03","cited_by_count":13},{"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":"2301.13371","title":"Demystifying Disagreement-on-the-Line in High Dimensions","created":"2023-01-31","cited_by_count":12},{"id":"2409.13904","title":"High-dimensional learning of narrow neural networks","created":"2024-09-20","cited_by_count":12},{"id":"2502.01763","title":"On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning","created":"2025-02-03","cited_by_count":12},{"id":"2305.12100","title":"How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features","created":"2023-05-20","cited_by_count":11},{"id":"2311.13774","title":"Learning Hierarchical Polynomials with Three-Layer Neural Networks","created":"2023-11-23","cited_by_count":9},{"id":"2505.18346","title":"On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective","created":"2025-05-23","cited_by_count":8},{"id":"2509.22214","title":"A Law of Data Reconstruction for Random Features (and Beyond)","created":"2025-09-26","cited_by_count":3},{"id":"2405.18274","title":"Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models","created":"2024-05-28","cited_by_count":2},{"id":"2510.21020","title":"From Information to Generative Exponent: Learning Rate Induces Phase Transitions in SGD","created":"2025-10-23","cited_by_count":2}],"next_cursor":null}