{"total":398,"rows":[{"id":"2202.10054","title":"Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution","created":"2022-02-21","cited_by_count":739,"area":null},{"id":"2102.06701","title":"Explaining Neural Scaling Laws","created":"2021-02-12","cited_by_count":363,"area":null},{"id":"2105.02375","title":"A Geometric Analysis of Neural Collapse with Unconstrained Features","created":"2021-05-06","cited_by_count":227,"area":null},{"id":"2105.12806","title":"A Universal Law of Robustness via Isoperimetry","created":"2021-05-26","cited_by_count":151,"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":"2105.14368","title":"Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation","created":"2021-05-29","cited_by_count":132,"area":null},{"id":"2402.01092","title":"A Dynamical Model of Neural Scaling Laws","created":"2024-02-02","cited_by_count":111,"area":null},{"id":"2101.10588","title":"Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration","created":"2021-01-26","cited_by_count":110,"area":null},{"id":"2105.15004","title":"Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime","created":"2021-05-31","cited_by_count":96,"area":null},{"id":"2202.06526","title":"Benign Overfitting in Two-layer Convolutional Neural Networks","created":"2022-02-14","cited_by_count":91,"area":null},{"id":"2203.06176","title":"More Than a Toy: Random Matrix Models Predict How Real-World Neural Representations Generalize","created":"2022-03-11","cited_by_count":91,"area":null},{"id":"2105.10446","title":"ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction","created":"2021-05-21","cited_by_count":84,"area":null},{"id":"2202.08832","title":"Universality of empirical risk minimization","created":"2022-02-17","cited_by_count":75,"area":null},{"id":"2103.01550","title":"Label-Imbalanced and Group-Sensitive Classification under Overparameterization","created":"2021-03-02","cited_by_count":74,"area":null},{"id":"2202.05928","title":"Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data","created":"2022-02-11","cited_by_count":74,"area":null},{"id":"2301.13105","title":"Generalization on the Unseen, Logic Reasoning and Degree Curriculum","created":"2023-01-30","cited_by_count":73,"area":null},{"id":"2302.05836","title":"Theory on Forgetting and Generalization of Continual Learning","created":"2023-02-12","cited_by_count":72,"area":null},{"id":"2102.11742","title":"Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed","created":"2021-02-23","cited_by_count":69,"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":"2209.14863","title":"Neural Networks Efficiently Learn Low-Dimensional Representations with SGD","created":"2022-09-29","cited_by_count":63,"area":null},{"id":"2109.02355","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","created":"2021-09-06","cited_by_count":57,"area":null},{"id":"2405.11751","title":"Asymptotic theory of in-context learning by linear attention","created":"2024-05-20","cited_by_count":55,"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":"2211.11567","title":"Neural networks trained with SGD learn distributions of increasing complexity","created":"2022-11-21","cited_by_count":50,"area":null},{"id":"2405.00592","title":"Scaling and renormalization in high-dimensional regression","created":"2024-05-01","cited_by_count":50,"area":null},{"id":"2104.13628","title":"Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures","created":"2021-04-28","cited_by_count":49,"area":null},{"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,"area":null},{"id":"2303.01372","title":"High-dimensional analysis of double descent for linear regression with random projections","created":"2023-03-02","cited_by_count":47,"area":null},{"id":"2204.10425","title":"Spectrum of inner-product kernel matrices in the polynomial regime and multiple descent phenomenon in kernel ridge regression","created":"2022-04-21","cited_by_count":45,"area":null},{"id":"2302.00375","title":"Bayes-optimal Learning of Deep Random Networks of Extensive-width","created":"2023-02-01","cited_by_count":41,"area":null},{"id":"2307.11353","title":"What can a Single Attention Layer Learn? A Study Through the Random Features Lens","created":"2023-07-21","cited_by_count":40,"area":null},{"id":"2201.11489","title":"The Implicit Bias of Benign Overfitting","created":"2022-01-27","cited_by_count":38,"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":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"area":null},{"id":"2303.14151","title":"Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle","created":"2023-03-24","cited_by_count":37,"area":null},{"id":"2306.13575","title":"Scaling MLPs: A Tale of Inductive Bias","created":"2023-06-23","cited_by_count":37,"area":null},{"id":"2205.10217","title":"Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization","created":"2022-05-20","cited_by_count":36,"area":null},{"id":"2207.06569","title":"Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting","created":"2022-07-14","cited_by_count":36,"area":null},{"id":"2303.15438","title":"On the Stepwise Nature of Self-Supervised Learning","created":"2023-03-27","cited_by_count":36,"area":null},{"id":"2102.04396","title":"SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality","created":"2021-02-08","cited_by_count":35,"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":"2106.08900","title":"Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality","created":"2021-06-14","cited_by_count":34,"area":null},{"id":"2107.11228","title":"Taxonomizing local versus global structure in neural network loss landscapes","created":"2021-07-23","cited_by_count":34,"area":null},{"id":"2104.04244","title":"How rotational invariance of common kernels prevents generalization in high dimensions","created":"2021-04-09","cited_by_count":32,"area":null},{"id":"2106.03212","title":"Towards an Understanding of Benign Overfitting in Neural Networks","created":"2021-06-06","cited_by_count":30,"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":"2303.01462","title":"Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization","created":"2023-03-02","cited_by_count":29,"area":null},{"id":"2202.03038","title":"Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry","created":"2022-02-07","cited_by_count":28,"area":null},{"id":"2209.15259","title":"On the Impossible Safety of Large AI Models","created":"2022-09-30","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":"2205.06308","title":"An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices with Polynomial Scalings","created":"2022-05-12","cited_by_count":27,"area":null},{"id":"2110.03922","title":"The Eigenlearning Framework: A Conservation Law Perspective on Kernel Regression and Wide Neural Networks","created":"2021-10-08","cited_by_count":26,"area":null},{"id":"2205.13303","title":"Gaussian Universality of Perceptrons with Random Labels","created":"2022-05-26","cited_by_count":26,"area":null},{"id":"2302.08923","title":"Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation","created":"2023-02-17","cited_by_count":26,"area":null},{"id":"2302.10886","title":"Some Fundamental Aspects about Lipschitz Continuity of Neural Networks","created":"2023-02-21","cited_by_count":26,"area":null},{"id":"2106.05418","title":"Probing transfer learning with a model of synthetic correlated datasets","created":"2021-06-09","cited_by_count":25,"area":null},{"id":"2212.12147","title":"The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes","created":"2022-12-23","cited_by_count":25,"area":null},{"id":"2112.03215","title":"Multi-scale Feature Learning Dynamics: Insights for Double Descent","created":"2021-12-06","cited_by_count":24,"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":"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":"2103.03191","title":"Generalization Bounds for Sparse Random Feature Expansions","created":"2021-03-04","cited_by_count":22,"area":null},{"id":"2205.14846","title":"Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression","created":"2022-05-30","cited_by_count":22,"area":null},{"id":"2210.13300","title":"Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis","created":"2022-10-24","cited_by_count":22,"area":null},{"id":"2302.06210","title":"Precise Asymptotic Analysis of Deep Random Feature Models","created":"2023-02-13","cited_by_count":22,"area":null},{"id":"2108.12006","title":"When and how epochwise double descent happens","created":"2021-08-26","cited_by_count":21,"area":null},{"id":"2109.09304","title":"Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks","created":"2021-09-20","cited_by_count":21,"area":null},{"id":"2110.00683","title":"Learning through atypical \"phase transitions\" in overparameterized neural networks","created":"2021-10-01","cited_by_count":21,"area":null},{"id":"2201.06142","title":"Towards Sample-efficient Overparameterized Meta-learning","created":"2022-01-16","cited_by_count":21,"area":null},{"id":"2204.06274","title":"Overparameterized Linear Regression under Adversarial Attacks","created":"2022-04-13","cited_by_count":21,"area":null},{"id":"2212.14457","title":"Bayesian Interpolation with Deep Linear Networks","created":"2022-12-29","cited_by_count":21,"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":"2307.06092","title":"Quantitative CLTs in Deep Neural Networks","created":"2023-07-12","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":"2509.24882","title":"Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime","created":"2025-09-29","cited_by_count":20,"area":null},{"id":"2201.13383","title":"Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension","created":"2022-01-31","cited_by_count":19,"area":null},{"id":"2205.06798","title":"Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime","created":"2022-05-13","cited_by_count":19,"area":null},{"id":"2206.01029","title":"Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions","created":"2022-06-02","cited_by_count":19,"area":null},{"id":"2307.11807","title":"Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks","created":"2023-07-21","cited_by_count":19,"area":null},{"id":"2502.02531","title":"Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer","created":"2025-02-04","cited_by_count":19,"area":null},{"id":"2102.07379","title":"On the Inherent Regularization Effects of Noise Injection During Training","created":"2021-02-15","cited_by_count":18,"area":null},{"id":"2108.11489","title":"The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks","created":"2021-08-25","cited_by_count":18,"area":null},{"id":"2202.03295","title":"Theoretical characterization of uncertainty in high-dimensional linear classification","created":"2022-02-07","cited_by_count":18,"area":null},{"id":"2310.18935","title":"Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data","created":"2023-10-29","cited_by_count":18,"area":null},{"id":"2311.14646","title":"More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory","created":"2023-11-24","cited_by_count":18,"area":null},{"id":"2110.11805","title":"Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model","created":"2021-10-22","cited_by_count":17,"area":null},{"id":"2106.03696","title":"Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models","created":"2021-06-07","cited_by_count":16,"area":null},{"id":"2110.08084","title":"Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization","created":"2021-10-15","cited_by_count":16,"area":null},{"id":"2111.05987","title":"Tight bounds for minimum l1-norm interpolation of noisy data","created":"2021-11-10","cited_by_count":16,"area":null},{"id":"2210.12760","title":"On double-descent in uncertainty quantification in overparametrized models","created":"2022-10-23","cited_by_count":16,"area":null},{"id":"2303.15809","title":"Kernel interpolation generalizes poorly","created":"2023-03-28","cited_by_count":16,"area":null},{"id":"2305.15141","title":"From Tempered to Benign Overfitting in ReLU Neural Networks","created":"2023-05-24","cited_by_count":16,"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":"2108.02883","title":"Interpolation can hurt robust generalization even when there is no noise","created":"2021-08-05","cited_by_count":15,"area":null},{"id":"2402.07745","title":"Predictive Churn with the Set of Good Models","created":"2024-02-12","cited_by_count":15,"area":null},{"id":"2104.14132","title":"Generalization Guarantees for Neural Architecture Search with Train-Validation Split","created":"2021-04-29","cited_by_count":14,"area":null},{"id":"2111.05198","title":"Harmless interpolation in regression and classification with structured features","created":"2021-11-09","cited_by_count":14,"area":null},{"id":"2112.03657","title":"Understanding Square Loss in Training Overparametrized Neural Network Classifiers","created":"2021-12-07","cited_by_count":14,"area":null},{"id":"2202.09889","title":"Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression","created":"2022-02-20","cited_by_count":14,"area":null},{"id":"2502.20003","title":"Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula","created":"2025-02-27","cited_by_count":14,"area":null}],"next_cursor":"100"}