{"total":92,"rows":[{"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":"2310.06110","title":"Grokking as the Transition from Lazy to Rich Training Dynamics","created":"2023-10-09","cited_by_count":108,"area":null},{"id":"2305.18270","title":"How Two-Layer Neural Networks Learn, One (Giant) Step at a Time","created":"2023-05-29","cited_by_count":68,"area":null},{"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,"area":null},{"id":"2303.00055","title":"Learning time-scales in two-layers neural networks","created":"2023-02-28","cited_by_count":57,"area":null},{"id":"2310.19793","title":"On Learning Gaussian Multi-index Models with Gradient Flow","created":"2023-10-30","cited_by_count":57,"area":null},{"id":"2306.07042","title":"Transformers learn through gradual rank increase","created":"2023-06-12","cited_by_count":52,"area":null},{"id":"2310.03789","title":"Grokking as a First Order Phase Transition in Two Layer Networks","created":"2023-10-05","cited_by_count":48,"area":null},{"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,"area":null},{"id":"2403.05529","title":"Computational-Statistical Gaps in Gaussian Single-Index Models","created":"2024-03-08","cited_by_count":44,"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":"2405.15459","title":"Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions","created":"2024-05-24","cited_by_count":37,"area":null},{"id":"2309.03843","title":"Gradient-Based Feature Learning under Structured Data","created":"2023-09-07","cited_by_count":32,"area":null},{"id":"2411.02544","title":"Pretrained transformer efficiently learns low-dimensional target functions in-context","created":"2024-11-04","cited_by_count":31,"area":null},{"id":"2604.21691","title":"There Will Be a Scientific Theory of Deep Learning","created":"2026-04-23","cited_by_count":29,"area":null},{"id":"2212.13881","title":"Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features","created":"2022-12-28","cited_by_count":25,"area":null},{"id":"2412.06540","title":"Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families","created":"2024-12-09","cited_by_count":24,"area":null},{"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,"area":null},{"id":"2308.13431","title":"Six Lectures on Linearized Neural Networks","created":"2023-08-25","cited_by_count":20,"area":null},{"id":"2305.06986","title":"Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks","created":"2023-05-11","cited_by_count":19,"area":null},{"id":"2307.15804","title":"On Single Index Models beyond Gaussian Data","created":"2023-07-28","cited_by_count":17,"area":null},{"id":"2410.04887","title":"Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse","created":"2024-10-07","cited_by_count":16,"area":null},{"id":"2305.18502","title":"Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD","created":"2023-05-29","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":"2408.07254","title":"Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics","created":"2024-08-14","cited_by_count":15,"area":null},{"id":"2504.05426","title":"Survey on Algorithms for multi-index models","created":"2025-04-07","cited_by_count":15,"area":null},{"id":"2310.12408","title":"Provable Guarantees for Neural Networks via Gradient Feature Learning","created":"2023-10-19","cited_by_count":14,"area":null},{"id":"2309.03800","title":"Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck","created":"2023-09-07","cited_by_count":13,"area":null},{"id":"2403.14917","title":"Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective","created":"2024-03-22","cited_by_count":13,"area":null},{"id":"2410.11275","title":"Shallow diffusion networks provably learn hidden low-dimensional structure","created":"2024-10-15","cited_by_count":12,"area":"diffusion"},{"id":"2405.09312","title":"Agnostic Active Learning of Single Index Models with Linear Sample Complexity","created":"2024-05-15","cited_by_count":11,"area":null},{"id":"2311.13774","title":"Learning Hierarchical Polynomials with Three-Layer Neural Networks","created":"2023-11-23","cited_by_count":9,"area":null},{"id":"2406.04657","title":"From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks","created":"2024-06-07","cited_by_count":9,"area":null},{"id":"2411.08798","title":"Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence","created":"2024-11-13","cited_by_count":9,"area":null},{"id":"2502.09525","title":"Robust Learning of Multi-index Models via Iterative Subspace Approximation","created":"2025-02-13","cited_by_count":9,"area":null},{"id":"2506.05500","title":"The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models","created":"2025-06-05","cited_by_count":9,"area":null},{"id":"2305.15598","title":"ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2311.01644","title":"Should Under-parameterized Student Networks Copy or Average Teacher Weights?","created":"2023-11-03","cited_by_count":8,"area":null},{"id":"2406.11110","title":"How Neural Networks Learn the Support is an Implicit Regularization Effect of SGD","created":"2024-06-17","cited_by_count":8,"area":null},{"id":"2406.17167","title":"Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis","created":"2024-06-24","cited_by_count":8,"area":null},{"id":"2502.06443","title":"Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions","created":"2025-02-10","cited_by_count":8,"area":null},{"id":"2312.15469","title":"Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products","created":"2023-12-24","cited_by_count":7,"area":null},{"id":"2403.02418","title":"The Role of the Time-Dependent Hessian in High-Dimensional Optimization","created":"2024-03-04","cited_by_count":7,"area":null},{"id":"2306.03335","title":"Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage","created":"2023-06-06","cited_by_count":6,"area":null},{"id":"2601.19756","title":"Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining","created":"2026-01-27","cited_by_count":6,"area":null},{"id":"2212.13848","title":"Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks","created":"2022-12-28","cited_by_count":5,"area":null},{"id":"2310.02117","title":"Symmetric Single Index Learning","created":"2023-10-03","cited_by_count":5,"area":null},{"id":"2310.05309","title":"Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods","created":"2023-10-08","cited_by_count":5,"area":null},{"id":"2410.05448","title":"Task Diversity Shortens the ICL Plateau","created":"2024-10-07","cited_by_count":5,"area":null},{"id":"2410.16449","title":"Robust Feature Learning for Multi-Index Models in High Dimensions","created":"2024-10-21","cited_by_count":5,"area":null},{"id":"2411.15385","title":"Gradient dynamics for low-rank fine-tuning beyond kernels","created":"2024-11-23","cited_by_count":5,"area":null},{"id":"2505.07070","title":"Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures","created":"2025-05-11","cited_by_count":5,"area":null},{"id":"2505.09167","title":"Online Learning of Neural Networks","created":"2025-05-14","cited_by_count":5,"area":null},{"id":"2602.10867","title":"Deep Learning of Compositional Targets with Hierarchical Spectral Methods","created":"2026-02-11","cited_by_count":5,"area":null},{"id":"2406.01766","title":"How Does Gradient Descent Learn Features -- A Local Analysis for Regularized Two-Layer Neural Networks","created":"2024-06-03","cited_by_count":4,"area":null},{"id":"2503.23642","title":"Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent","created":"2025-03-31","cited_by_count":4,"area":null},{"id":"2512.04165","title":"Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity","created":"2025-12-03","cited_by_count":4,"area":null},{"id":"2503.11272","title":"When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective","created":"2025-03-14","cited_by_count":3,"area":null},{"id":"2509.25741","title":"Test time training enhances in-context learning of nonlinear functions","created":"2025-09-30","cited_by_count":3,"area":null},{"id":"2601.19208","title":"How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability","created":"2026-01-27","cited_by_count":3,"area":"mechinterp"},{"id":"2602.08907","title":"Positive Distribution Shift as a Framework for Understanding Tractable Learning","created":"2026-02-09","cited_by_count":3,"area":null},{"id":"2510.07205","title":"Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts","created":"2025-10-08","cited_by_count":2,"area":"moe"},{"id":"2510.21020","title":"From Information to Generative Exponent: Learning Rate Induces Phase Transitions in SGD","created":"2025-10-23","cited_by_count":2,"area":null},{"id":"2601.00455","title":"Deep Networks Learn Deep Hierarchical Models","created":"2026-01-01","cited_by_count":2,"area":null},{"id":"2305.13141","title":"Tight conditions for when the NTK approximation is valid","created":"2023-05-22","cited_by_count":1,"area":null},{"id":"2401.07606","title":"RedEx: Beyond Fixed Representation Methods via Convex Optimization","created":"2024-01-15","cited_by_count":1,"area":null},{"id":"2411.17567","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2510.12026","title":"Mamba Can Learn Low-Dimensional Targets In-Context via Test-Time Feature Learning","created":"2025-10-14","cited_by_count":1,"area":"ssm"},{"id":"2512.15987","title":"Provably Extracting the Features from a General Superposition","created":"2025-12-17","cited_by_count":1,"area":"mechinterp"},{"id":"2601.07061","title":"Local EGOP for Continuous Index Learning","created":"2026-01-11","cited_by_count":1,"area":null},{"id":"2602.17876","title":"Interactive Learning of Single-Index Models via Stochastic Gradient Descent","created":"2026-02-19","cited_by_count":1,"area":null},{"id":"2603.11161","title":"Algorithmic Task Capture, Computational Complexity, and Inductive Bias of Infinite Transformers","created":"2026-03-11","cited_by_count":1,"area":null},{"id":"2604.03068","title":"Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks","created":"2026-04-03","cited_by_count":1,"area":null},{"id":"2605.05710","title":"On the Blessing of Pre-training in Weak-to-Strong Generalization","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2605.09454","title":"Optimal Regret for Single Index Bandits","created":"2026-05-10","cited_by_count":1,"area":null},{"id":"2605.10237","title":"The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2605.12908","title":"The Mechanism of Weak-to-Strong Generalization: Feature Elicitation from Latent Knowledge","created":"2026-05-13","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":"2605.29684","title":"Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime","created":"2026-05-28","cited_by_count":1,"area":null},{"id":"2303.14244","title":"Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing","created":"2023-03-24","cited_by_count":0,"area":null},{"id":"2504.01875","title":"AYLA: Architecting a loss landscape in shallow neural networks to accelerate feature recovery","created":"2025-04-02","cited_by_count":0,"area":null},{"id":"2504.08489","title":"Statistically guided deep learning","created":"2025-04-11","cited_by_count":0,"area":null},{"id":"2505.12353","title":"Importance Sampling for Nonlinear Models","created":"2025-05-18","cited_by_count":0,"area":null},{"id":"2506.11357","title":"Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2510.15174","title":"A simple mean field model of feature learning","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2510.19382","title":"A Derandomization Framework for Structure Discovery: Applications in Neural Networks and Beyond","created":"2025-10-22","cited_by_count":0,"area":null},{"id":"2602.00172","title":"Neuron Block Dynamics for XOR Classification with Zero-Margin","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.07378","title":"Dichotomy of Feature Learning and Unlearning: Fast-Slow Analysis on Neural Networks with Stochastic Gradient Descent","created":"2026-02-07","cited_by_count":0,"area":"safety"},{"id":"2605.24749","title":"How Neural Reward Models Learn Features for Policy Optimization: A Single-Index Analysis","created":"2026-05-23","cited_by_count":0,"area":"alignment"},{"id":"2607.06369","title":"Feature Learning for the High Dimensional Stationary Sch\\\"odinger Equation with Deep Ritz Method","created":"2026-07-07","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},{"id":"2609.12994","title":"Dimension-Corrected Hitting Times for Heavy-Tailed Spectral Emergence in Neural Optimizer Dynamics","created":"2026-07-10","cited_by_count":0,"area":null}],"next_cursor":null}