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[1] Inderjit S. Dhillon,et al. Recovery Guarantees for One-hidden-layer Neural Networks , 2017, ICML.
[2] Liwei Wang,et al. Gradient Descent Finds Global Minima of Deep Neural Networks , 2018, ICML.
[3] Adam R. Klivans,et al. Learning Depth-Three Neural Networks in Polynomial Time , 2017, ArXiv.
[4] C. D. Boor,et al. Polynomial interpolation in several variables , 1994 .
[5] Tengyu Ma,et al. Identity Matters in Deep Learning , 2016, ICLR.
[6] Thomas Laurent,et al. Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global , 2017, ICML.
[7] Matthias Hein,et al. The Loss Surface of Deep and Wide Neural Networks , 2017, ICML.
[8] Chen Ling,et al. The Best Rank-1 Approximation of a Symmetric Tensor and Related Spherical Optimization Problems , 2012, SIAM J. Matrix Anal. Appl..
[9] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[10] Leslie Pack Kaelbling,et al. Bayesian Optimization with Exponential Convergence , 2015, NIPS.
[11] Matthias Hein,et al. On the loss landscape of a class of deep neural networks with no bad local valleys , 2018, ICLR.
[12] Adam R. Klivans,et al. Learning Neural Networks with Two Nonlinear Layers in Polynomial Time , 2017, COLT.
[13] Yoshua Bengio,et al. Depth with Nonlinearity Creates No Bad Local Minima in ResNets , 2019, Neural Networks.
[14] Kenji Kawaguchi,et al. Every Local Minimum Value Is the Global Minimum Value of Induced Model in Nonconvex Machine Learning , 2019, Neural Computation.
[15] Yuanzhi Li,et al. Convergence Analysis of Two-layer Neural Networks with ReLU Activation , 2017, NIPS.
[16] Alex Lamb,et al. Deep Learning for Classical Japanese Literature , 2018, ArXiv.
[17] Jiaoyang Huang,et al. Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes , 2019, 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[18] Gang Wang,et al. Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization , 2018, IEEE Transactions on Signal Processing.
[19] Wei Hu,et al. Width Provably Matters in Optimization for Deep Linear Neural Networks , 2019, ICML.
[20] Yuan Cao,et al. Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks , 2018, ArXiv.
[21] Katta G. Murty,et al. Some NP-complete problems in quadratic and nonlinear programming , 1987, Math. Program..
[22] Yann LeCun,et al. The Loss Surfaces of Multilayer Networks , 2014, AISTATS.
[23] Matthias Hein,et al. Optimization Landscape and Expressivity of Deep CNNs , 2017, ICML.
[24] Philip M. Long,et al. Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks , 2018, Neural Computation.
[25] Ohad Shamir,et al. Are ResNets Provably Better than Linear Predictors? , 2018, NeurIPS.
[26] Ronald L. Rivest,et al. Training a 3-node neural network is NP-complete , 1988, COLT '88.
[27] Leslie Pack Kaelbling,et al. Effect of Depth and Width on Local Minima in Deep Learning , 2018, Neural Computation.
[28] Anima Anandkumar,et al. Provable Methods for Training Neural Networks with Sparse Connectivity , 2014, ICLR.
[29] Kenji Kawaguchi,et al. Deep Learning without Poor Local Minima , 2016, NIPS.
[30] Elad Hoffer,et al. Exponentially vanishing sub-optimal local minima in multilayer neural networks , 2017, ICLR.
[31] Mahdi Soltanolkotabi,et al. Learning ReLUs via Gradient Descent , 2017, NIPS.
[32] Tengyu Ma,et al. Learning One-hidden-layer Neural Networks with Landscape Design , 2017, ICLR.
[33] Jason D. Lee,et al. On the Power of Over-parametrization in Neural Networks with Quadratic Activation , 2018, ICML.
[34] Quanquan Gu,et al. An Improved Analysis of Training Over-parameterized Deep Neural Networks , 2019, NeurIPS.
[35] Alexandr Andoni,et al. Learning Polynomials with Neural Networks , 2014, ICML.
[36] R. Srikant,et al. Adding One Neuron Can Eliminate All Bad Local Minima , 2018, NeurIPS.
[37] Amir Globerson,et al. Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs , 2017, ICML.
[38] Yuanzhi Li,et al. A Convergence Theory for Deep Learning via Over-Parameterization , 2018, ICML.