{"total":126,"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":"2402.01092","title":"A Dynamical Model of Neural Scaling Laws","created":"2024-02-02","cited_by_count":111,"area":null},{"id":"2205.09653","title":"Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks","created":"2022-05-19","cited_by_count":105,"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":"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":"2405.11751","title":"Asymptotic theory of in-context learning by linear attention","created":"2024-05-20","cited_by_count":55,"area":null},{"id":"2402.03220","title":"The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents","created":"2024-02-05","cited_by_count":52,"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":"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":"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":"2302.00375","title":"Bayes-optimal Learning of Deep Random Networks of Extensive-width","created":"2023-02-01","cited_by_count":41,"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":"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":"2302.00401","title":"Deterministic equivalent and error universality of deep random features learning","created":"2023-02-01","cited_by_count":35,"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":"2604.21691","title":"There Will Be a Scientific Theory of Deep Learning","created":"2026-04-23","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":"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":"2310.16441","title":"Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding","created":"2023-10-25","cited_by_count":25,"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":"2602.07488","title":"Deriving Neural Scaling Laws from the statistics of natural language","created":"2026-02-07","cited_by_count":23,"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":"2302.06210","title":"Precise Asymptotic Analysis of Deep Random Feature Models","created":"2023-02-13","cited_by_count":22,"area":null},{"id":"2410.18837","title":"High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws","created":"2024-10-24","cited_by_count":22,"area":"distill"},{"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":"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":"2304.07235","title":"Mapping of attention mechanisms to a generalized Potts model","created":"2023-04-14","cited_by_count":19,"area":null},{"id":"2305.11041","title":"High-dimensional Asymptotics of Denoising Autoencoders","created":"2023-05-18","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":"2202.03295","title":"Theoretical characterization of uncertainty in high-dimensional linear classification","created":"2022-02-07","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":"2203.12094","title":"Learning curves for the multi-class teacher-student perceptron","created":"2022-03-22","cited_by_count":17,"area":"distill"},{"id":"2208.01003","title":"What Can Be Learnt With Wide Convolutional Neural Networks?","created":"2022-08-01","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":"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":"2309.12821","title":"A Spectral Theory of Neural Prediction and Alignment","created":"2023-09-22","cited_by_count":14,"area":null},{"id":"2409.18061","title":"Optimal Protocols for Continual Learning via Statistical Physics and Control Theory","created":"2024-09-26","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},{"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":"2303.00564","title":"Learning curves for deep structured Gaussian feature models","created":"2023-03-01","cited_by_count":13,"area":null},{"id":"2402.01297","title":"Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum","created":"2024-02-02","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":"2202.04925","title":"Decomposing neural networks as mappings of correlation functions","created":"2022-02-10","cited_by_count":12,"area":null},{"id":"2301.13371","title":"Demystifying Disagreement-on-the-Line in High Dimensions","created":"2023-01-31","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":"2206.11124","title":"A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta","created":"2022-06-22","cited_by_count":11,"area":null},{"id":"2210.07612","title":"Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes","created":"2022-10-14","cited_by_count":11,"area":null},{"id":"2210.12082","title":"A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models","created":"2022-10-21","cited_by_count":11,"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":"2305.12100","title":"How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features","created":"2023-05-20","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":"2303.02644","title":"Expectation consistency for calibration of neural networks","created":"2023-03-05","cited_by_count":10,"area":null},{"id":"2310.18280","title":"Universality for the global spectrum of random inner-product kernel matrices in the polynomial regime","created":"2023-10-27","cited_by_count":10,"area":null},{"id":"2501.06074","title":"Geometry and Optimization of Shallow Polynomial Networks","created":"2025-01-10","cited_by_count":10,"area":"distill"},{"id":"2205.15935","title":"Bias-inducing geometries: an exactly solvable data model with fairness implications","created":"2022-05-31","cited_by_count":9,"area":null},{"id":"2305.06435","title":"Phase transitions in the mini-batch size for sparse and dense two-layer neural networks","created":"2023-05-10","cited_by_count":9,"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":"2402.16770","title":"Neural population geometry and optimal coding of tasks with shared latent structure","created":"2024-02-26","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":"2409.19659","title":"Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery","created":"2024-09-29","cited_by_count":9,"area":"distill"},{"id":"2410.09005","title":"Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra","created":"2024-10-11","cited_by_count":9,"area":null},{"id":"2502.01347","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","created":"2025-02-03","cited_by_count":9,"area":null},{"id":"2505.24849","title":"Statistical mechanics of extensive-width Bayesian neural networks near interpolation","created":"2025-05-30","cited_by_count":9,"area":null},{"id":"2404.18769","title":"Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks","created":"2024-04-29","cited_by_count":8,"area":null},{"id":"2408.04607","title":"Risk and cross validation in ridge regression with correlated samples","created":"2024-08-08","cited_by_count":8,"area":null},{"id":"2211.06077","title":"Overparameterized random feature regression with nearly orthogonal data","created":"2022-11-11","cited_by_count":7,"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":"2502.05074","title":"Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2310.00176","title":"Universality of max-margin classifiers","created":"2023-09-29","cited_by_count":6,"area":null},{"id":"2310.13434","title":"Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption","created":"2023-10-20","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":"2306.13185","title":"An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression","created":"2023-06-22","cited_by_count":5,"area":null},{"id":"2309.07663","title":"High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve","created":"2023-09-14","cited_by_count":5,"area":null},{"id":"2206.01332","title":"Optimal Activation Functions for the Random Features Regression Model","created":"2022-05-31","cited_by_count":4,"area":null},{"id":"2206.08933","title":"A theory of learning with constrained weight-distribution","created":"2022-06-14","cited_by_count":4,"area":null},{"id":"2303.09995","title":"Neural-prior stochastic block model","created":"2023-03-17","cited_by_count":4,"area":null},{"id":"2305.14689","title":"Least Squares Regression Can Exhibit Under-Parameterized Double Descent","created":"2023-05-24","cited_by_count":4,"area":null},{"id":"2402.13622","title":"Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression","created":"2024-02-21","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":"2204.02678","title":"Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves","created":"2022-04-06","cited_by_count":3,"area":null},{"id":"2212.13468","title":"Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods","created":"2022-12-27","cited_by_count":3,"area":null},{"id":"2309.16846","title":"Optimal Nonlinearities Improve Generalization Performance of Random Features","created":"2023-09-28","cited_by_count":3,"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":"2410.18858","title":"Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional Tokens","created":"2024-10-24","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":"2512.00196","title":"Emergent Riemannian geometry over learning discrete computations on continuous manifolds","created":"2025-11-28","cited_by_count":3,"area":null},{"id":"2605.22341","title":"A Boundary-Layer Mechanism for One-Third Scaling in Online Softmax Classification","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2210.11589","title":"Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization","created":"2022-10-20","cited_by_count":2,"area":null},{"id":"2306.05679","title":"Bayes optimal learning in high-dimensional linear regression with network side information","created":"2023-06-09","cited_by_count":2,"area":null},{"id":"2405.18274","title":"Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Models","created":"2024-05-28","cited_by_count":2,"area":null},{"id":"2411.17807","title":"A solvable generative model with a linear, one-step denoiser","created":"2024-11-26","cited_by_count":2,"area":"diffusion"},{"id":"2501.16226","title":"The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model","created":"2025-01-27","cited_by_count":2,"area":"distill"}],"next_cursor":"100"}