{"total":60,"rows":[{"id":"2201.02177","title":"Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets","created":"2022-01-06","cited_by_count":629,"area":null},{"id":"2210.01117","title":"Omnigrok: Grokking Beyond Algorithmic Data","created":"2022-10-03","cited_by_count":146,"area":null},{"id":"2112.00029","title":"Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models","created":"2021-11-30","cited_by_count":76,"area":null},{"id":"2103.10948","title":"The Shape of Learning Curves: a Review","created":"2021-03-19","cited_by_count":70,"area":null},{"id":"2405.00592","title":"Scaling and renormalization in high-dimensional regression","created":"2024-05-01","cited_by_count":50,"area":null},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"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":"2206.10012","title":"Limitations of the NTK for Understanding Generalization in Deep Learning","created":"2022-06-20","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":"2107.11228","title":"Taxonomizing local versus global structure in neural network loss landscapes","created":"2021-07-23","cited_by_count":34,"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":"2210.15435","title":"Grokking phase transitions in learning local rules with gradient descent","created":"2022-10-26","cited_by_count":28,"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":"2106.02261","title":"Out-of-Distribution Generalization in Kernel Regression","created":"2021-06-04","cited_by_count":21,"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":"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":"2103.05524","title":"On the interplay between data structure and loss function in classification problems","created":"2021-03-09","cited_by_count":13,"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":"2307.07785","title":"The Interpolating Information Criterion for Overparameterized Models","created":"2023-07-15","cited_by_count":12,"area":null},{"id":"2405.08707","title":"Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory","created":"2024-05-14","cited_by_count":12,"area":null},{"id":"2106.00116","title":"Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest Images","created":"2021-05-31","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":"2208.09897","title":"Multiple Descent in the Multiple Random Feature Model","created":"2022-08-21","cited_by_count":10,"area":null},{"id":"2106.02630","title":"Fundamental tradeoffs between memorization and robustness in random features and neural tangent regimes","created":"2021-06-04","cited_by_count":9,"area":null},{"id":"2203.11355","title":"Origami in N dimensions: How feed-forward networks manufacture linear separability","created":"2022-03-21","cited_by_count":9,"area":null},{"id":"2208.03848","title":"Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality","created":"2022-08-08","cited_by_count":9,"area":null},{"id":"2311.04787","title":"Why Do Probabilistic Clinical Models Fail To Transport Between Sites?","created":"2023-11-08","cited_by_count":9,"area":null},{"id":"2107.12685","title":"On the Role of Optimization in Double Descent: A Least Squares Study","created":"2021-07-27","cited_by_count":7,"area":null},{"id":"2110.09502","title":"Minimum $\\ell_{1}$-norm interpolators: Precise asymptotics and multiple descent","created":"2021-10-18","cited_by_count":7,"area":null},{"id":"2203.05443","title":"Bias-variance decomposition of overparameterized regression with random linear features","created":"2022-03-10","cited_by_count":7,"area":null},{"id":"2402.10164","title":"Random features and polynomial rules","created":"2024-02-15","cited_by_count":6,"area":null},{"id":"2212.06757","title":"Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures","created":"2022-12-13","cited_by_count":5,"area":null},{"id":"2103.14108","title":"The Geometry of Over-parameterized Regression and Adversarial Perturbations","created":"2021-03-25","cited_by_count":4,"area":null},{"id":"2112.00980","title":"Trap of Feature Diversity in the Learning of MLPs","created":"2021-12-02","cited_by_count":4,"area":null},{"id":"2204.07293","title":"Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees","created":"2022-04-15","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":"2107.00797","title":"Mitigating deep double descent by concatenating inputs","created":"2021-07-02","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":"2112.03660","title":"A generalization gap estimation for overparameterized models via the Langevin functional variance","created":"2021-12-07","cited_by_count":2,"area":null},{"id":"2211.14061","title":"A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance","created":"2022-11-25","cited_by_count":2,"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":"2504.03035","title":"High-dimensional ridge regression with random features for non-identically distributed data with a variance profile","created":"2025-04-03","cited_by_count":2,"area":null},{"id":"2505.01751","title":"A dynamic view of some anomalous phenomena in SGD","created":"2025-05-03","cited_by_count":2,"area":null},{"id":"2103.00500","title":"Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks","created":"2021-02-28","cited_by_count":1,"area":null},{"id":"2203.10204","title":"Inferring topological transitions in pattern-forming processes with self-supervised learning","created":"2022-03-19","cited_by_count":1,"area":null},{"id":"2511.04514","title":"Linear Mode Connectivity under Data Shifts for Deep Ensembles of Image Classifiers","created":"2025-11-06","cited_by_count":1,"area":null},{"id":"2201.09223","title":"A Generalized Weighted Optimization Method for Computational Learning and Inversion","created":"2022-01-23","cited_by_count":0,"area":null},{"id":"2201.12803","title":"Generalizing similarity in noisy setups: the DIBS phenomenon","created":"2022-01-30","cited_by_count":0,"area":null},{"id":"2302.07419","title":"Spatially heterogeneous learning by a deep student machine","created":"2023-02-15","cited_by_count":0,"area":null},{"id":"2306.05583","title":"Gibbs-Based Information Criteria and the Over-Parameterized Regime","created":"2023-06-08","cited_by_count":0,"area":null},{"id":"2312.03951","title":"Understanding the Role of Optimization in Double Descent","created":"2023-12-06","cited_by_count":0,"area":null},{"id":"2401.12610","title":"The twin peaks of learning neural networks","created":"2024-01-23","cited_by_count":0,"area":null},{"id":"2508.17412","title":"A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization","created":"2025-08-24","cited_by_count":0,"area":null},{"id":"2511.18517","title":"Foundations of Artificial Intelligence Frameworks: Notion and Limits of AGI","created":"2025-11-23","cited_by_count":0,"area":null},{"id":"2601.08316","title":"Deep Exploration of Epoch-wise Double Descent in Noisy Data: Signal Separation, Large Activation, and Benign Overfitting","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2605.04231","title":"Anatomy of a failure: When, how, and why deep vision fails in scientific domains","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2607.26414","title":"Origins and mitigation of double descent in reduced order modeling","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2610.04507","title":"LoRA's Second Descent Extends Beyond Parameter Parity","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}