Learning ReLUs via Gradient Descent
暂无分享,去创建一个
[1] Benjamin Recht,et al. Sharp Time–Data Tradeoffs for Linear Inverse Problems , 2015, IEEE Transactions on Information Theory.
[2] Yuandong Tian,et al. An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis , 2017, ICML.
[3] Adam Tauman Kalai,et al. The Isotron Algorithm: High-Dimensional Isotonic Regression , 2009, COLT.
[4] Joel A. Tropp,et al. Living on the edge: phase transitions in convex programs with random data , 2013, 1303.6672.
[5] Adel Javanmard,et al. Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks , 2017, IEEE Transactions on Information Theory.
[6] YoungJu Choie,et al. Local minima and back propagation , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[7] Geoffrey E. Hinton,et al. Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[8] Yuanzhi Li,et al. Convergence Analysis of Two-layer Neural Networks with ReLU Activation , 2017, NIPS.
[9] W. Härdle,et al. Direct Semiparametric Estimation of Single-Index Models with Discrete Covariates dpsfb950075.ps.tar = Enno MAMMEN J.S. MARRON: Mass Recentered Kernel Smoothers , 1996 .
[10] Amir Globerson,et al. Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs , 2017, ICML.
[11] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[12] Pablo A. Parrilo,et al. The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.
[13] Y. Gordon. On Milman's inequality and random subspaces which escape through a mesh in ℝ n , 1988 .
[14] Samet Oymak,et al. Fast and Reliable Parameter Estimation from Nonlinear Observations , 2016, SIAM J. Optim..
[15] H. Ichimura,et al. SEMIPARAMETRIC LEAST SQUARES (SLS) AND WEIGHTED SLS ESTIMATION OF SINGLE-INDEX MODELS , 1993 .
[16] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[17] Mahdi Soltanolkotabi,et al. Structured Signal Recovery From Quadratic Measurements: Breaking Sample Complexity Barriers via Nonconvex Optimization , 2017, IEEE Transactions on Information Theory.
[18] Inderjit S. Dhillon,et al. Recovery Guarantees for One-hidden-layer Neural Networks , 2017, ICML.
[19] Matthias Hein,et al. The Loss Surface of Deep and Wide Neural Networks , 2017, ICML.
[20] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[21] Robert D. Nowak,et al. Learning Single Index Models in High Dimensions , 2015, ArXiv.
[22] Xiaodong Li,et al. Phase Retrieval via Wirtinger Flow: Theory and Algorithms , 2014, IEEE Transactions on Information Theory.
[23] Adam Tauman Kalai,et al. Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression , 2011, NIPS.
[24] Varun Kanade,et al. Reliably Learning the ReLU in Polynomial Time , 2016, COLT.