{"total":28,"rows":[{"id":"2108.01538","title":"Geometry of Linear Convolutional Networks","created":"2021-08-03","cited_by_count":27,"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":"2202.04777","title":"Exact Solutions of a Deep Linear Network","created":"2022-02-10","cited_by_count":24,"area":null},{"id":"2212.14457","title":"Bayesian Interpolation with Deep Linear Networks","created":"2022-12-29","cited_by_count":21,"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":"2304.05752","title":"Function Space and Critical Points of Linear Convolutional Networks","created":"2023-04-12","cited_by_count":19,"area":null},{"id":"2402.07193","title":"Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent","created":"2024-02-11","cited_by_count":19,"area":null},{"id":"2205.04009","title":"Posterior Collapse of a Linear Latent Variable Model","created":"2022-05-09","cited_by_count":18,"area":null},{"id":"2512.20607","title":"Saddle-to-Saddle Dynamics Explains A Simplicity Bias Across Neural Network Architectures","created":"2025-12-23","cited_by_count":15,"area":null},{"id":"2102.09671","title":"When Are Solutions Connected in Deep Networks?","created":"2021-02-18","cited_by_count":11,"area":null},{"id":"2408.11979","title":"Only Strict Saddles in the Energy Landscape of Predictive Coding Networks?","created":"2024-08-21","cited_by_count":10,"area":null},{"id":"2412.06289","title":"S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity","created":"2024-12-09","cited_by_count":10,"area":null},{"id":"2404.06391","title":"Exploring Neural Network Landscapes: Star-Shaped and Geodesic Connectivity","created":"2024-04-09","cited_by_count":9,"area":null},{"id":"2210.12001","title":"When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work","created":"2022-10-21","cited_by_count":8,"area":null},{"id":"2102.13233","title":"Spurious Local Minima Are Common for Deep Neural Networks with Piecewise Linear Activations","created":"2021-02-25","cited_by_count":7,"area":null},{"id":"2206.01515","title":"Understanding Deep Learning via Decision Boundary","created":"2022-06-03","cited_by_count":7,"area":null},{"id":"2207.01269","title":"DiffML: End-to-end Differentiable ML Pipelines","created":"2022-07-04","cited_by_count":7,"area":null},{"id":"2303.03027","title":"Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss","created":"2023-03-06","cited_by_count":7,"area":null},{"id":"2204.08306","title":"A Convergence Analysis of Nesterov's Accelerated Gradient Method in Training Deep Linear Neural Networks","created":"2022-04-18","cited_by_count":4,"area":null},{"id":"2204.07261","title":"Convergence and Implicit Regularization Properties of Gradient Descent for Deep Residual Networks","created":"2022-04-14","cited_by_count":3,"area":null},{"id":"2102.04338","title":"The Landscape of Multi-Layer Linear Neural Network From the Perspective of Algebraic Geometry","created":"2021-01-30","cited_by_count":2,"area":null},{"id":"2505.10678","title":"System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach","created":"2025-05-15","cited_by_count":2,"area":null},{"id":"2506.20344","title":"A Complete Loss Landscape Analysis of Regularized Deep Matrix Factorization","created":"2025-06-25","cited_by_count":2,"area":null},{"id":"2201.06658","title":"Learning Neural Ranking Models Online from Implicit User Feedback","created":"2022-01-17","cited_by_count":1,"area":null},{"id":"2402.08948","title":"Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input","created":"2024-02-14","cited_by_count":1,"area":null},{"id":"2103.07287","title":"Neural Networks with Complex-Valued Weights Have No Spurious Local Minima","created":"2021-01-31","cited_by_count":0,"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":"2609.13057","title":"Benign Loss Landscapes Can Coexist with Worst-Case Hardness","created":"2026-09-11","cited_by_count":0,"area":null}],"next_cursor":null}