Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods

9 January 2021

Papers citing "Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods"

10 / 60 papers shown

Title
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 428 43,234 0 11 Feb 2015
Difference Target Propagation Dong-Hyun Lee Saizheng Zhang Asja Fischer Yoshua Bengio AAML 79 348 0 23 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 239 19,017 0 20 Dec 2014
Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks David Balduzzi Hastagiri P. Vanchinathan J. M. Buhmann FAtt 56 71 0 23 Nov 2014
Deeply-Supervised Nets Chen-Yu Lee Saining Xie Patrick W. Gallagher Zhengyou Zhang Zhuowen Tu 327 2,238 0 18 Sep 2014
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.5K 39,472 0 01 Sep 2014
How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation Yoshua Bengio 95 184 0 29 Jul 2014
Distributed optimization of deeply nested systems M. A. Carreira-Perpiñán Weiran Wang 97 194 0 24 Dec 2012
Similarity Learning for Provably Accurate Sparse Linear Classification A. Bellet Amaury Habrard M. Sebban 49 57 0 27 Jun 2012
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 235 12,422 0 24 Jun 2012