{"total":25,"rows":[{"id":"2211.11567","title":"Neural networks trained with SGD learn distributions of increasing complexity","created":"2022-11-21","cited_by_count":50,"area":null},{"id":"2302.00401","title":"Deterministic equivalent and error universality of deep random features learning","created":"2023-02-01","cited_by_count":35,"area":null},{"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,"area":null},{"id":"2309.16476","title":"High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality","created":"2023-09-28","cited_by_count":15,"area":null},{"id":"2402.13999","title":"Asymptotics of Learning with Deep Structured (Random) Features","created":"2024-02-21","cited_by_count":13,"area":null},{"id":"2406.03260","title":"Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers","created":"2024-06-05","cited_by_count":12,"area":null},{"id":"2409.13904","title":"High-dimensional learning of narrow neural networks","created":"2024-09-20","cited_by_count":12,"area":null},{"id":"2304.02912","title":"Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach","created":"2023-04-06","cited_by_count":11,"area":null},{"id":"2306.14975","title":"The Underlying Scaling Laws and Universal Statistical Structure of Complex Datasets","created":"2023-06-26","cited_by_count":9,"area":null},{"id":"2402.05674","title":"A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs","created":"2024-02-08","cited_by_count":7,"area":null},{"id":"2410.06717","title":"Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks","created":"2024-10-09","cited_by_count":6,"area":null},{"id":"2410.05609","title":"The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures","created":"2024-10-08","cited_by_count":4,"area":null},{"id":"2410.16073","title":"On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds","created":"2024-10-21","cited_by_count":4,"area":null},{"id":"2510.10693","title":"High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator","created":"2025-10-12","cited_by_count":4,"area":null},{"id":"2401.17269","title":"Effect of Weight Quantization on Learning Models by Typical Case Analysis","created":"2024-01-30","cited_by_count":3,"area":null},{"id":"2409.20250","title":"Input-Label Correlation Governs a Linear-to-Nonlinear Transition in Random Features under Spiked Covariance","created":"2024-09-30","cited_by_count":2,"area":null},{"id":"2506.12454","title":"On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification","created":"2025-06-14","cited_by_count":2,"area":null},{"id":"2602.02855","title":"When pre-training hurts LoRA fine-tuning: a dynamical analysis via single-index models","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2603.17896","title":"A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models","created":"2026-03-18","cited_by_count":2,"area":null},{"id":"2608.08390","title":"Lasso Universality Under Linearly Dependent Covariates in the Sparse Regime","created":"2026-08-09","cited_by_count":2,"area":null},{"id":"2412.06545","title":"On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks","created":"2024-12-09","cited_by_count":1,"area":null},{"id":"2604.03146","title":"Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization","created":"2026-04-03","cited_by_count":1,"area":null},{"id":"2605.10795","title":"Factual recall in linear associative memories: sharp asymptotics and mechanistic insights","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2608.10184","title":"The sharp SAT/UNSAT phase transition in random ellipsoid fitting","created":"2026-08-10","cited_by_count":1,"area":null},{"id":"2608.22361","title":"Dataset Complexity Shapes Finite-Distance Loss Geometry in Neural Networks","created":"2026-08-23","cited_by_count":1,"area":null}],"next_cursor":null}