{"total":27,"rows":[{"id":"2105.01650","title":"Stochastic gradient descent with noise of machine learning type. Part I: Discrete time analysis","created":"2021-05-04","cited_by_count":39,"area":null},{"id":"2407.21078","title":"Convergence rates for the Adam optimizer","created":"2024-07-29","cited_by_count":15,"area":null},{"id":"2408.09966","title":"Mask in the Mirror: Implicit Sparsification","created":"2024-08-19","cited_by_count":14,"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":"2108.04620","title":"A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions","created":"2021-08-10","cited_by_count":10,"area":null},{"id":"2104.00277","title":"A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions","created":"2021-04-01","cited_by_count":9,"area":null},{"id":"2108.08106","title":"Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation","created":"2021-08-18","cited_by_count":9,"area":null},{"id":"2112.07369","title":"Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions","created":"2021-12-13","cited_by_count":9,"area":null},{"id":"2302.03550","title":"Exponential convergence rates for momentum stochastic gradient descent in the overparametrized setting","created":"2023-02-07","cited_by_count":8,"area":null},{"id":"2504.19426","title":"Sharp higher order convergence rates for the Adam optimizer","created":"2025-04-28","cited_by_count":8,"area":null},{"id":"2404.12293","title":"Singular-limit analysis of gradient descent with noise injection","created":"2024-04-18","cited_by_count":7,"area":null},{"id":"2302.14690","title":"On the existence of minimizers in shallow residual ReLU neural network optimization landscapes","created":"2023-02-28","cited_by_count":6,"area":null},{"id":"2303.03950","title":"On the existence of optimal shallow feedforward networks with ReLU activation","created":"2023-03-06","cited_by_count":5,"area":null},{"id":"2305.05828","title":"A Normal Map-Based Proximal Stochastic Gradient Method: Convergence and Identification Properties","created":"2023-05-10","cited_by_count":5,"area":null},{"id":"2505.09572","title":"SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures","created":"2025-05-14","cited_by_count":5,"area":null},{"id":"2312.01047","title":"A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization","created":"2023-12-02","cited_by_count":4,"area":null},{"id":"2410.16849","title":"Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2505.11434","title":"Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization","created":"2025-05-16","cited_by_count":3,"area":null},{"id":"2103.04392","title":"A Retrospective Approximation Approach for Smooth Stochastic Optimization","created":"2021-03-07","cited_by_count":1,"area":null},{"id":"2512.02303","title":"Training Dynamics of Learning 3D-Rotational Equivariance","created":"2025-12-02","cited_by_count":1,"area":null},{"id":"2607.04233","title":"Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks","created":"2026-07-05","cited_by_count":1,"area":null},{"id":"2203.10973","title":"A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima","created":"2022-03-21","cited_by_count":0,"area":null},{"id":"2206.13646","title":"On bounds for norms of reparameterized ReLU artificial neural network parameters: sums of fractional powers of the Lipschitz norm control the network parameter vector","created":"2022-06-27","cited_by_count":0,"area":null},{"id":"2412.06070","title":"Stochastic Gradient Descent Revisited","created":"2024-12-08","cited_by_count":0,"area":null},{"id":"2501.00389","title":"Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs","created":"2024-12-31","cited_by_count":0,"area":null},{"id":"2504.08867","title":"In almost all shallow analytic neural network optimization landscapes, efficient minimizers have strongly convex neighborhoods","created":"2025-04-11","cited_by_count":0,"area":null},{"id":"2512.10188","title":"The Interplay of Statistics and Noisy Optimization: Learning Linear Predictors with Random Data Weights","created":"2025-12-11","cited_by_count":0,"area":null}],"next_cursor":null}