Polynomial Convergence of Gradient Descent for Training One-Hidden-Layer Neural Networks

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Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers (2018, background): “1 What Can Neural Networks Provably Learn? Most existing works analyzing the learnability of neural networks [9, 12, 13, 20, 21, 28, 33, 34, 42, 43, 47, 49, 50, 56] make unrealistic assumptions about the data distribution (such as being random Gaussian), and/or make strong assumptions about the netw”
On the Convergence Rate of Training Recurrent Neural Networks (2018, background): “On the technique side, some of these results try to understand the gradient dynamics [8, 9, 14, 16, 38, 44, 59, 64, 65, 70, 71], while others focus on the geometry properties of the training objective [19, 20, 25, 50, 72].”
Making Method of Moments Great Again? -- How can GANs learn the target distribution (2020, background): “The recent advance in deep learning [5, 6] that extends the study of two-layer neural networks [44, 68, 73, 35, 67, 70, 25, 79, 53, 24, 50, 72, 22, 78, 47, 49] might make this direction a not-so-remote goal.”
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK (2020, background): “…(2016); Soudry and Carmon (2016); Xie et al. (2016); Soltanolkotabi et al. (2017); Tian (2017); Brutzkus and Globerson (2017); Boob and Lan (2017); Vempala and Wilmes (2018); Oymak and Soltanolkotabi (2019); Bakshi et al. (2018); Yehudai and Shamir (2019); Zhang et al. (2018); Li and Liang…”