{"total":17,"rows":[{"id":"2106.15933","title":"Saddle-to-Saddle Dynamics in Deep Linear Networks: Small Initialization Training, Symmetry, and Sparsity","created":"2021-06-30","cited_by_count":85,"area":null},{"id":"2410.02984","title":"Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient","created":"2024-10-03","cited_by_count":40,"area":null},{"id":"2107.06608","title":"Continuous vs. Discrete Optimization of Deep Neural Networks","created":"2021-07-14","cited_by_count":34,"area":null},{"id":"2111.01875","title":"Subquadratic Overparameterization for Shallow Neural Networks","created":"2021-11-02","cited_by_count":26,"area":null},{"id":"2107.13289","title":"The loss landscape of deep linear neural networks: a second-order analysis","created":"2021-07-28","cited_by_count":24,"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":"2306.01154","title":"The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks","created":"2023-06-01","cited_by_count":20,"area":null},{"id":"2202.12262","title":"On the Omnipresence of Spurious Local Minima in Certain Neural Network Training Problems","created":"2022-02-23","cited_by_count":8,"area":null},{"id":"2307.01177","title":"Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space","created":"2023-07-03","cited_by_count":5,"area":null},{"id":"2311.05061","title":"Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics","created":"2023-11-08","cited_by_count":3,"area":null},{"id":"2112.13269","title":"Over-Parametrized Matrix Factorization in the Presence of Spurious Stationary Points","created":"2021-12-25","cited_by_count":1,"area":null},{"id":"2305.09904","title":"On the ISS Property of the Gradient Flow for Single Hidden-Layer Neural Networks with Linear Activations","created":"2023-05-17","cited_by_count":1,"area":null},{"id":"2408.13767","title":"Lecture Notes on Linear Neural Networks: A Tale of Optimization and Generalization in Deep Learning","created":"2024-08-25","cited_by_count":1,"area":null},{"id":"2503.23641","title":"Remarks on the Polyak-Lojasiewicz inequality and the convergence of gradient systems","created":"2025-03-31","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":"2201.02761","title":"Global Convergence Analysis of Deep Linear Networks with A One-neuron Layer","created":"2022-01-08","cited_by_count":0,"area":null},{"id":"2511.09810","title":"On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks","created":"2025-11-12","cited_by_count":0,"area":null}],"next_cursor":null}