{"total":69,"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":"2205.01445","title":"High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation","created":"2022-05-03","cited_by_count":150,"area":null},{"id":"2302.11055","title":"SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics","created":"2023-02-21","cited_by_count":143,"area":null},{"id":"2210.12067","title":"Efficient Dataset Distillation Using Random Feature Approximation","created":"2022-10-21","cited_by_count":111,"area":null},{"id":"2310.06110","title":"Grokking as the Transition from Lazy to Rich Training Dynamics","created":"2023-10-09","cited_by_count":108,"area":null},{"id":"2206.04030","title":"High-dimensional limit theorems for SGD: Effective dynamics and critical scaling","created":"2022-06-08","cited_by_count":70,"area":null},{"id":"2209.14863","title":"Neural Networks Efficiently Learn Low-Dimensional Representations with SGD","created":"2022-09-29","cited_by_count":63,"area":null},{"id":"2106.03791","title":"Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions","created":"2021-06-07","cited_by_count":54,"area":null},{"id":"2202.00293","title":"Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks","created":"2022-02-01","cited_by_count":50,"area":null},{"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":"2302.05882","title":"From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks","created":"2023-02-12","cited_by_count":43,"area":null},{"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,"area":null},{"id":"2402.04980","title":"Asymptotics of feature learning in two-layer networks after one gradient-step","created":"2024-02-07","cited_by_count":38,"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":"2106.04110","title":"A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs","created":"2021-06-08","cited_by_count":33,"area":null},{"id":"2309.03843","title":"Gradient-Based Feature Learning under Structured Data","created":"2023-09-07","cited_by_count":32,"area":null},{"id":"2211.06506","title":"Spectral Evolution and Invariance in Linear-width Neural Networks","created":"2022-11-11","cited_by_count":30,"area":null},{"id":"2202.00565","title":"Data-driven emergence of convolutional structure in neural networks","created":"2022-02-01","cited_by_count":29,"area":null},{"id":"2302.08933","title":"Universality laws for Gaussian mixtures in generalized linear models","created":"2023-02-17","cited_by_count":28,"area":null},{"id":"2201.02115","title":"The dynamics of representation learning in shallow, non-linear autoencoders","created":"2022-01-06","cited_by_count":27,"area":null},{"id":"2203.00573","title":"Contrasting random and learned features in deep Bayesian linear regression","created":"2022-03-01","cited_by_count":24,"area":null},{"id":"2405.16418","title":"Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective","created":"2024-05-26","cited_by_count":24,"area":"diffusion"},{"id":"2206.12314","title":"Learning sparse features can lead to overfitting in neural networks","created":"2022-06-24","cited_by_count":23,"area":null},{"id":"2305.01604","title":"The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold","created":"2023-05-02","cited_by_count":21,"area":null},{"id":"2308.13431","title":"Six Lectures on Linearized Neural Networks","created":"2023-08-25","cited_by_count":20,"area":null},{"id":"2205.09029","title":"Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation","created":"2022-05-18","cited_by_count":16,"area":null},{"id":"2106.03227","title":"Neural Tangent Kernel Maximum Mean Discrepancy","created":"2021-06-06","cited_by_count":15,"area":null},{"id":"2407.05622","title":"On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries","created":"2024-07-08","cited_by_count":15,"area":null},{"id":"2410.13300","title":"A theoretical perspective on mode collapse in variational inference","created":"2024-10-17","cited_by_count":14,"area":null},{"id":"2501.03937","title":"A solvable model of learning generative diffusion: theory and insights","created":"2025-01-07","cited_by_count":14,"area":"diffusion"},{"id":"2302.05440","title":"Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization","created":"2023-02-10","cited_by_count":13,"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":"2105.02468","title":"Relative stability toward diffeomorphisms indicates performance in deep nets","created":"2021-05-06","cited_by_count":12,"area":null},{"id":"2404.08602","title":"Sliding down the stairs: how correlated latent variables accelerate learning with neural networks","created":"2024-04-12","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":"2205.14818","title":"Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods","created":"2022-05-30","cited_by_count":10,"area":"distill"},{"id":"2310.01975","title":"Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data","created":"2023-10-03","cited_by_count":10,"area":null},{"id":"2402.05271","title":"Feature learning as alignment: a structural property of gradient descent in non-linear neural networks","created":"2024-02-07","cited_by_count":10,"area":null},{"id":"2502.03210","title":"From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning","created":"2025-02-05","cited_by_count":10,"area":null},{"id":"2106.03485","title":"Redundant representations help generalization in wide neural networks","created":"2021-06-07","cited_by_count":9,"area":null},{"id":"2406.01589","title":"Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks","created":"2024-06-03","cited_by_count":9,"area":null},{"id":"2201.08082","title":"Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions","created":"2022-01-20","cited_by_count":8,"area":null},{"id":"2107.14324","title":"Deep Networks Provably Classify Data on Curves","created":"2021-07-29","cited_by_count":6,"area":null},{"id":"2402.10164","title":"Random features and polynomial rules","created":"2024-02-15","cited_by_count":6,"area":null},{"id":"2306.10404","title":"The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions","created":"2023-06-17","cited_by_count":5,"area":null},{"id":"2212.11826","title":"The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning","created":"2022-12-22","cited_by_count":4,"area":null},{"id":"2406.12615","title":"When Are Bias-Free ReLU Networks Effectively Linear Networks?","created":"2024-06-18","cited_by_count":4,"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":"2503.00856","title":"Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure","created":"2025-03-02","cited_by_count":4,"area":null},{"id":"2312.14922","title":"Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks","created":"2023-12-22","cited_by_count":3,"area":null},{"id":"2507.07907","title":"A statistical physics framework for optimal learning","created":"2025-07-10","cited_by_count":3,"area":null},{"id":"2510.05573","title":"On the Theory of Continual Learning with Gradient Descent for Neural Networks","created":"2025-10-07","cited_by_count":3,"area":null},{"id":"2512.13634","title":"Universality of high-dimensional scaling limits of stochastic gradient descent","created":"2025-12-15","cited_by_count":3,"area":null},{"id":"2306.12190","title":"Quantifying lottery tickets under label noise: accuracy, calibration, and complexity","created":"2023-06-21","cited_by_count":2,"area":null},{"id":"2410.16150","title":"Modeling Structured Data Learning with Restricted Boltzmann Machines in the Teacher-Student Setting","created":"2024-10-21","cited_by_count":2,"area":"distill"},{"id":"2606.20299","title":"Statistical Properties of Training & Generalization","created":"2026-06-18","cited_by_count":2,"area":null},{"id":"2205.14553","title":"Long-Tailed Learning Requires Feature Learning","created":"2022-05-29","cited_by_count":1,"area":null},{"id":"2205.14683","title":"The impact of memory on learning sequence-to-sequence tasks","created":"2022-05-29","cited_by_count":1,"area":null},{"id":"2211.09184","title":"An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks","created":"2022-11-16","cited_by_count":1,"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":"2505.24060","title":"Characterising the Inductive Biases of Neural Networks on Boolean Data","created":"2025-05-29","cited_by_count":1,"area":null},{"id":"2507.19680","title":"Feature learning is decoupled from generalization in high capacity neural networks","created":"2025-07-25","cited_by_count":1,"area":null},{"id":"2605.14927","title":"Learning with Shallow Neural Networks on Cluster-Structured Features","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2609.38998","title":"Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks","created":"2026-09-30","cited_by_count":1,"area":null},{"id":"2405.18427","title":"Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2603.03071","title":"From Reachability to Learnability: Geometric Design Principles for Quantum Neural Networks","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2606.31110","title":"Explaining Machine Learning and Memorization with Statistical Mechanics","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.00207","title":"Homogenization of $\\ell_2$-Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.10869","title":"Singular perturbations and hierarchical learning in two-layer neural networks","created":"2026-07-12","cited_by_count":0,"area":null}],"next_cursor":null}