Learning Neural Networks with Two Nonlinear Layers in Polynomial Time

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Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian (2019, background): “Generalization properties of stochastic gradient descent is extensively studied empirically [34, 28, 50, 16, 29, 24, 25].”
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers (2018, methodology): “We acknowledge a different line of research using kernels as improper learners to learn the concept class of neural networks [22, 23, 36, 54].”
Learning Two-layer Neural Networks with Symmetric Inputs (2018, methodology): “This is considered to be a three-layer network if one counts the number of layers for nodes (e.g. in Goel and Klivans (2017)) or a one-hidden layer network if one just counts the number of hidden layers. ar X iv :1 81 0.”
Optimization Landscape and Expressivity of Deep CNNs (2017, unknown): “…(Andoni et al., 2014; Sedghi & Anandkumar, 2015; Janzamin et al., 2016; Gautier et al., 2016; Haeffele & Vidal, 2017; Soudry & Hoffer, 2017; Goel & Klivans, 2017; Li & Yuan, 2017; Zhong et al., 2017; Tian, 2017; Brutzkus & Globerson, 2017; Soltanolkotabi, 2017; Du et al., 2018) in order to…”