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Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation.

, and . CoRR, (2020)

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Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation., and . CoRR, (2020)Convergence and Sample Complexity of Gradient Methods for the Model-Free Linear-Quadratic Regulator Problem., , , and . IEEE Trans. Autom. Control., 67 (5): 2435-2450 (2022)Structured signal recovery from quadratic measurements: Breaking sample complexity barriers via nonconvex optimization.. CoRR, (2017)A Field Guide to Federated Optimization., , , , , , , , , and 43 other author(s). CoRR, (2021)Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks., , and . IEEE Trans. Inf. Theory, 65 (2): 742-769 (2019)Learning from many trajectories., , and . CoRR, (2022)FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks., , , , , , , , , and 1 other author(s). CoRR, (2021)On the lack of gradient domination for linear quadratic Gaussian problems with incomplete state information., , and . CDC, page 1120-1124. IEEE, (2021)PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 4150-4159. PMLR, (2021)CUDA: Convolution-Based Unlearnable Datasets., , and . CVPR, page 3862-3871. IEEE, (2023)