{"total":110,"rows":[{"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":"2206.06232","title":"Towards Understanding Sharpness-Aware Minimization","created":"2022-06-13","cited_by_count":146,"area":null},{"id":"2110.06914","title":"What Happens after SGD Reaches Zero Loss? --A Mathematical Framework","created":"2021-10-13","cited_by_count":107,"area":null},{"id":"2302.07011","title":"A Modern Look at the Relationship between Sharpness and Generalization","created":"2023-02-14","cited_by_count":92,"area":null},{"id":"2208.12591","title":"On the Implicit Bias in Deep-Learning Algorithms","created":"2022-08-26","cited_by_count":86,"area":null},{"id":"2206.00939","title":"Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs","created":"2022-06-02","cited_by_count":76,"area":null},{"id":"2410.09754","title":"SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning","created":"2024-10-13","cited_by_count":76,"area":"deeprl"},{"id":"2411.18704","title":"Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits","created":"2024-11-27","cited_by_count":71,"area":null},{"id":"2210.05337","title":"SGD with Large Step Sizes Learns Sparse Features","created":"2022-10-11","cited_by_count":68,"area":null},{"id":"2207.02628","title":"The alignment property of SGD noise and how it helps select flat minima: A stability analysis","created":"2022-07-06","cited_by_count":64,"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":"2304.00488","title":"Saddle-to-Saddle Dynamics in Diagonal Linear Networks","created":"2023-04-02","cited_by_count":57,"area":null},{"id":"2210.03403","title":"TAN Without a Burn: Scaling Laws of DP-SGD","created":"2022-10-07","cited_by_count":52,"area":null},{"id":"2305.17490","title":"The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent","created":"2023-05-27","cited_by_count":44,"area":null},{"id":"2403.02241","title":"Neural Redshift: Random Networks are not Random Functions","created":"2024-03-04","cited_by_count":43,"area":null},{"id":"2502.04664","title":"Implicit Bias of Spectral Descent and Muon on Multiclass Separable Data","created":"2025-02-07","cited_by_count":41,"area":null},{"id":"2207.04036","title":"Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent","created":"2022-07-08","cited_by_count":39,"area":null},{"id":"2306.04251","title":"Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks","created":"2023-06-07","cited_by_count":39,"area":null},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"area":null},{"id":"2302.08982","title":"(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability","created":"2023-02-17","cited_by_count":36,"area":null},{"id":"2206.09841","title":"Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation","created":"2022-06-20","cited_by_count":29,"area":null},{"id":"2305.16038","title":"Implicit bias of SGD in $L_{2}$-regularized linear DNNs: One-way jumps from high to low rank","created":"2023-05-25","cited_by_count":29,"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":"2208.14673","title":"Incremental Learning in Diagonal Linear Networks","created":"2022-08-31","cited_by_count":28,"area":null},{"id":"2201.11729","title":"Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks","created":"2022-01-27","cited_by_count":27,"area":null},{"id":"2206.04613","title":"Explicit Regularization in Overparametrized Models via Noise Injection","created":"2022-06-09","cited_by_count":26,"area":null},{"id":"2408.15417","title":"Implicit Geometry of Next-token Prediction: From Language Sparsity Patterns to Model Representations","created":"2024-08-27","cited_by_count":24,"area":null},{"id":"2206.07252","title":"Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions","created":"2022-06-15","cited_by_count":23,"area":null},{"id":"2305.13064","title":"Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond","created":"2023-05-22","cited_by_count":21,"area":null},{"id":"2211.16980","title":"Infinite-width limit of deep linear neural networks","created":"2022-11-29","cited_by_count":20,"area":null},{"id":"2402.07193","title":"Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent","created":"2024-02-11","cited_by_count":19,"area":null},{"id":"2405.17580","title":"Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes","created":"2024-05-27","cited_by_count":18,"area":null},{"id":"2205.13647","title":"Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures","created":"2022-05-26","cited_by_count":17,"area":null},{"id":"2402.05787","title":"How do Transformers perform In-Context Autoregressive Learning?","created":"2024-02-08","cited_by_count":17,"area":null},{"id":"2210.02720","title":"Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias","created":"2022-10-06","cited_by_count":16,"area":null},{"id":"2402.18551","title":"Implicit Optimization Bias of Next-Token Prediction in Linear Models","created":"2024-02-28","cited_by_count":14,"area":null},{"id":"2405.20763","title":"Improving Generalization and Convergence by Enhancing Implicit Regularization","created":"2024-05-31","cited_by_count":14,"area":null},{"id":"2408.09966","title":"Mask in the Mirror: Implicit Sparsification","created":"2024-08-19","cited_by_count":14,"area":null},{"id":"2502.02496","title":"Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries","created":"2025-02-04","cited_by_count":14,"area":null},{"id":"2506.06489","title":"Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks","created":"2025-06-06","cited_by_count":14,"area":null},{"id":"2410.04642","title":"The Optimization Landscape of SGD Across the Feature Learning Strength","created":"2024-10-06","cited_by_count":13,"area":null},{"id":"2309.10688","title":"On the different regimes of Stochastic Gradient Descent","created":"2023-09-19","cited_by_count":12,"area":null},{"id":"2504.12883","title":"Mirror, Mirror of the Flow: How Does Regularization Shape Implicit Bias?","created":"2025-04-17","cited_by_count":12,"area":null},{"id":"2209.11002","title":"Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing","created":"2022-09-22","cited_by_count":11,"area":null},{"id":"2505.17958","title":"The Nuclear Route: Sharp Asymptotics of ERM in Overparameterized Quadratic Networks","created":"2025-05-23","cited_by_count":11,"area":null},{"id":"2205.15142","title":"Special Properties of Gradient Descent with Large Learning Rates","created":"2022-05-30","cited_by_count":10,"area":null},{"id":"2301.13703","title":"Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning","created":"2023-01-31","cited_by_count":9,"area":null},{"id":"2310.00692","title":"A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent","created":"2023-10-01","cited_by_count":9,"area":null},{"id":"2310.02396","title":"Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse","created":"2023-10-03","cited_by_count":9,"area":null},{"id":"2403.05293","title":"Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks","created":"2024-03-08","cited_by_count":9,"area":null},{"id":"2406.12763","title":"Implicit Bias of Mirror Flow on Separable Data","created":"2024-06-18","cited_by_count":9,"area":null},{"id":"2505.11846","title":"Learning on a Razor's Edge: Identifiability and Singularity of Polynomial Neural Networks","created":"2025-05-17","cited_by_count":9,"area":null},{"id":"2306.11680","title":"The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks","created":"2023-06-20","cited_by_count":8,"area":null},{"id":"2202.13328","title":"Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization","created":"2022-02-27","cited_by_count":7,"area":null},{"id":"2404.12293","title":"Singular-limit analysis of gradient descent with noise injection","created":"2024-04-18","cited_by_count":7,"area":null},{"id":"2302.02904","title":"Rethinking Gauss-Newton for learning over-parameterized models","created":"2023-02-06","cited_by_count":6,"area":null},{"id":"2401.11600","title":"Understanding the Generalization Benefits of Late Learning Rate Decay","created":"2024-01-21","cited_by_count":6,"area":null},{"id":"2403.11963","title":"Transfer Learning Beyond Bounded Density Ratios","created":"2024-03-18","cited_by_count":6,"area":null},{"id":"2504.12801","title":"Sign-In to the Lottery: Reparameterizing Sparse Training From Scratch","created":"2025-04-17","cited_by_count":6,"area":null},{"id":"2210.16400","title":"Flatter, faster: scaling momentum for optimal speedup of SGD","created":"2022-10-28","cited_by_count":5,"area":null},{"id":"2406.07361","title":"Deep Implicit Optimization enables Robust Learnable Features for Deformable Image Registration","created":"2024-06-11","cited_by_count":5,"area":null},{"id":"2410.08309","title":"Swing-by Dynamics in Concept Learning and Compositional Generalization","created":"2024-10-10","cited_by_count":5,"area":"diffusion"},{"id":"2505.05409","title":"Hide & Seek: Transformer Symmetries Obscure Sharpness & Riemannian Geometry Finds It","created":"2025-05-08","cited_by_count":5,"area":null},{"id":"2506.06940","title":"Understanding Sharpness Dynamics in NN Training with a Minimalist Example: The Effects of Dataset Difficulty, Depth, Stochasticity, and More","created":"2025-06-07","cited_by_count":5,"area":null},{"id":"2602.20062","title":"A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning","created":"2026-02-23","cited_by_count":5,"area":null},{"id":"2307.07410","title":"Implicit regularization in AI meets generalized hardness of approximation in optimization -- Sharp results for diagonal linear networks","created":"2023-07-13","cited_by_count":4,"area":null},{"id":"2311.14387","title":"Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling","created":"2023-11-24","cited_by_count":4,"area":null},{"id":"2402.07875","title":"Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States","created":"2024-02-12","cited_by_count":4,"area":"deeprl"},{"id":"2410.16401","title":"Simplicity Bias via Global Convergence of Sharpness Minimization","created":"2024-10-21","cited_by_count":4,"area":null},{"id":"2505.21722","title":"Saddle-To-Saddle Dynamics in Deep ReLU Networks: Low-Rank Bias in the First Saddle Escape","created":"2025-05-27","cited_by_count":4,"area":null},{"id":"2509.18766","title":"Diagonal Linear Networks and the Lasso Regularization Path","created":"2025-09-23","cited_by_count":4,"area":null},{"id":"2603.02064","title":"Never Saddle for Reparameterized Steepest Descent as Mirror Flow","created":"2026-03-02","cited_by_count":4,"area":null},{"id":"2303.17805","title":"On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks","created":"2023-03-31","cited_by_count":3,"area":null},{"id":"2311.06138","title":"Minimum norm interpolation by perceptra: Explicit regularization and implicit bias","created":"2023-11-10","cited_by_count":3,"area":null},{"id":"2311.15051","title":"Gradient Descent with Polyak's Momentum Finds Flatter Minima via Large Catapults","created":"2023-11-25","cited_by_count":3,"area":null},{"id":"2403.08585","title":"Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems","created":"2024-03-13","cited_by_count":3,"area":null},{"id":"2406.20006","title":"On the Trade-off between Flatness and Optimization in Distributed Learning","created":"2024-06-28","cited_by_count":3,"area":null},{"id":"2407.02322","title":"Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent","created":"2024-07-02","cited_by_count":3,"area":null},{"id":"2407.09845","title":"Towards Understanding Epoch-wise Double descent in Two-layer Linear Neural Networks","created":"2024-07-13","cited_by_count":3,"area":null},{"id":"2410.14158","title":"A Mirror Descent Perspective of Smoothed Sign Descent","created":"2024-10-18","cited_by_count":3,"area":null},{"id":"2601.06597","title":"Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective","created":"2026-01-10","cited_by_count":3,"area":null},{"id":"2605.19458","title":"Implicit Bias of Mirror Flow in Homogeneous Neural Networks: Sparse and Dense Feature Learning","created":"2026-05-19","cited_by_count":3,"area":null},{"id":"2402.08344","title":"Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training","created":"2024-02-13","cited_by_count":2,"area":null},{"id":"2406.02769","title":"Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks","created":"2024-06-04","cited_by_count":2,"area":null},{"id":"2412.16765","title":"Optimization Insights into Deep Diagonal Linear Networks","created":"2024-12-21","cited_by_count":2,"area":null},{"id":"2505.11370","title":"Understanding Nonlinear Implicit Bias via Region Counts in Input Space","created":"2025-05-16","cited_by_count":2,"area":null},{"id":"2510.01930","title":"Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory","created":"2025-10-02","cited_by_count":2,"area":null},{"id":"2601.01465","title":"Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD","created":"2026-01-04","cited_by_count":2,"area":null},{"id":"2601.06961","title":"The Impact of Anisotropic Covariance Structure on the Training Dynamics and Generalization Error of Linear Networks","created":"2026-01-11","cited_by_count":2,"area":null},{"id":"2402.07626","title":"Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features","created":"2024-02-12","cited_by_count":1,"area":null},{"id":"2508.15086","title":"Wormhole Dynamics in Deep Neural Networks","created":"2025-08-20","cited_by_count":1,"area":null},{"id":"2509.21614","title":"Effective continuous equations for adaptive SGD: a stochastic analysis view","created":"2025-09-25","cited_by_count":1,"area":null},{"id":"2602.05065","title":"Does SGD Seek Flatness or Sharpness? An Exactly Solvable Model","created":"2026-02-04","cited_by_count":1,"area":null},{"id":"2602.12257","title":"On the implicit regularization of Langevin dynamics with projected noise","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2603.10079","title":"Large Spikes in Stochastic Gradient Descent: A Large-Deviations View","created":"2026-03-10","cited_by_count":1,"area":null},{"id":"2606.05957","title":"Dead Directions: Geometric Singular Learning","created":"2026-06-04","cited_by_count":1,"area":null},{"id":"2606.14929","title":"Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts","created":"2026-06-12","cited_by_count":1,"area":null},{"id":"2606.20469","title":"Fisher-Geometric Sharpness and the Implicit Bias of SGD toward Flat Minima","created":"2026-06-18","cited_by_count":1,"area":null},{"id":"2607.12332","title":"Gradient Flow Dynamics and Implicit Bias of Diagonal Linear Networks under Infinitesimal Initialization","created":"2026-07-14","cited_by_count":1,"area":null},{"id":"2412.17613","title":"Can Stability be Detrimental? Better Generalization through Gradient Descent Instabilities","created":"2024-12-23","cited_by_count":0,"area":null}],"next_cursor":"100"}