Minimal Effort Back Propagation for Convolutional Neural Networks

Supersab: fast adaptive back propagation with good scaling properties

Tollenaere, Tom · 1990

Earlier work this paper cites.

A direct adaptive method for faster backpropagation learning: The rprop algorithm

Riedmiller, Martin and Braun, Heinrich · 1993

Earlier work this paper cites.

Natural image statistics and efficient coding

Olshausen, Bruno A and Field, David J · 1996

Earlier work this paper cites.

Efficient learning of sparse representations with an energy-based model

Poultney, Christopher, Chopra, Sumit, Cun, Yann L, et al · 2007

Earlier work this paper cites.

Modeling latent-dynamic in shallow parsing: A latent conditional model with improved inference

Sun, Xu, Morency, Louis-Philippe, Okanohara, Daisuke, and Tsujii, Jun’ichi · 2008

Earlier work this paper cites.

A large scale ranker-based system for search query spelling correction

Gao, Jianfeng, Li, Xiaolong, Micol, Daniel, Quirk, Chris, and Sun, Xu · 2010

Earlier work this paper cites.

Mnist handwritten digit database

LeCun, Yann, Cortes, Corinna, and Burges, Christopher JC · 2010

Earlier work this paper cites.

Learning phrase-based spelling error models from clickthrough data

Sun, Xu, Gao, Jianfeng, Micol, Daniel, and Quirk, Chris · 2010

Earlier work this paper cites.

Deep sparse rectifier neural networks

Glorot, Xavier, Bordes, Antoine, and Bengio, Yoshua · 2011

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A convergence analysis of log-linear training

Wiesler, Simon and Ney, Hermann · 2011

Cited alongside, same era.

Imagenet classification with deep convolutional neural networks

Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012

Cited alongside, same era.

Efficient backprop

LeCun, Yann A, Bottou, Léon, Orr, Genevieve B, and Müller, Klaus-Robert · 2012

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Fast online training with frequency-adaptive learning rates for chinese word segmentation and new word detection

Sun, Xu, Wang, Houfeng, and Li, Wenjie · 2012

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On using very large target vocabulary for neural machine translation

Original

Jean, Sébastien, Cho, Kyunghyun, Memisevic, Roland, and Bengio, Yoshua · 2014

Dropout: a simple way to prevent neural networks from overfitting

Srivastava, Nitish, Hinton, Geoffrey E, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014

Later among the works it cites.

Deepface: Closing the gap to human-level performance in face verification

Taigman, Yaniv, Yang, Ming, Ranzato, Marc’Aurelio, and Wolf, Lior · 2014

Later among the works it cites.

TensorFlow: Large-scale machine learning on heterogeneous systems, 2015

Abadi, Martín, Agarwal, Ashish, Barham, Paul, Brevdo, Eugene, Chen, Zhifeng, Citro, Craig, Corrado, Greg S., Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Ghemawat, Sanjay, Goodfellow, Ian, Harp, Andrew, Irving, Geoffrey, Isard, Michael, Jia, Yangqing, Jozefowicz, Rafal, Kaiser, Lukasz, Kudlur, Manjunath, Levenberg, Josh, Mané, Dan, Monga, Rajat, Moore, Sherry, Murray, Derek, Olah, Chris, Schuster, Mike, Shlens, Jonathon, Steiner, Benoit, Sutskever, Ilya, Talwar, Kunal, Tucker, Paul, Vanhoucke, Vincent, Vasudevan, Vijay, Viégas, Fernanda, Vinyals, Oriol, Warden, Pete, Wattenberg, Martin, Wicke, Martin, Yu, Yuan, and Zheng, Xiaoqiang · 2015

Later among the works it cites.

Batch normalization: Accelerating deep network training by reducing internal covariate shift

Ioffe, Sergey and Szegedy, Christian · 2015

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