{"total":22,"rows":[{"id":"2201.07395","title":"Overview frequency principle/spectral bias in deep learning","created":"2022-01-19","cited_by_count":69,"area":null},{"id":"2207.05952","title":"Implicit regularization of dropout","created":"2022-07-13","cited_by_count":25,"area":null},{"id":"2501.08537","title":"Complexity Control Facilitates Reasoning-Based Compositional Generalization in Transformers","created":"2025-01-15","cited_by_count":20,"area":null},{"id":"2205.12101","title":"Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width","created":"2022-05-24","cited_by_count":17,"area":null},{"id":"2405.05409","title":"Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing","created":"2024-05-08","cited_by_count":17,"area":null},{"id":"2504.09484","title":"An overview of condensation phenomenon in deep learning","created":"2025-04-13","cited_by_count":17,"area":null},{"id":"2303.06561","title":"Phase Diagram of Initial Condensation for Two-layer Neural Networks","created":"2023-03-12","cited_by_count":16,"area":null},{"id":"2305.12133","title":"Loss Spike in Training Neural Networks","created":"2023-05-20","cited_by_count":16,"area":null},{"id":"2112.09684","title":"On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks","created":"2021-12-17","cited_by_count":12,"area":null},{"id":"2402.05155","title":"Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2305.09947","title":"Understanding the Initial Condensation of Convolutional Neural Networks","created":"2023-05-17","cited_by_count":8,"area":null},{"id":"2205.13283","title":"Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks","created":"2022-05-26","cited_by_count":7,"area":null},{"id":"2211.11623","title":"Linear Stability Hypothesis and Rank Stratification for Nonlinear Models","created":"2022-11-21","cited_by_count":7,"area":null},{"id":"2307.08921","title":"Optimistic Estimate Uncovers the Potential of Nonlinear Models","created":"2023-07-18","cited_by_count":5,"area":null},{"id":"2402.15958","title":"On the dynamics of three-layer neural networks: initial condensation","created":"2024-02-25","cited_by_count":3,"area":null},{"id":"2405.13721","title":"Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion","created":"2024-05-22","cited_by_count":3,"area":null},{"id":"2506.14951","title":"Flat Channels to Infinity in Neural Loss Landscapes","created":"2025-06-17","cited_by_count":3,"area":null},{"id":"2602.05600","title":"On the Superlinear Relationship between SGD Noise Covariance and Loss Landscape Curvature","created":"2026-02-05","cited_by_count":2,"area":null},{"id":"2406.18035","title":"Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization","created":"2024-06-26","cited_by_count":0,"area":null},{"id":"2505.13582","title":"Uncovering Critical Sets of Deep Neural Networks via Sample-Independent Critical Lifting","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2606.04327","title":"A Geometric Characterization of the Stationary Plateau for Two-Layer Neural Networks","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2610.04533","title":"Weight Decay and Neuron Condensation: A Three-Stage Analysis of Two-Layer ReLU Networks","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}