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Neural networks enjoy widespread use, but many aspects of their training, representation, and operation are poorly understood.
Beitrag zur näherungweisen integration totaler differentialgleichungen
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Simpson’s rule
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
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Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
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Qualitatively characterizing neural network optimization problems
Ian J Goodfellow, Oriol Vinyals, and Andrew M Saxe · 2014
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Very deep convolutional networks for large-scale image recognition
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Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, and Yann LeCun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Understanding Neural Networks Through Deep Visualization
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson · 2015
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G. Alain and Y. Bengio · 2016
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Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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On the quality of the initial basin in overspecified neural networks
Itay Safran and Ohad Shamir · 2016
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Improved multitask learning through synaptic intelligence
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Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2018
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Fix your classifier: the marginal value of training the last weight layer
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2018
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Measuring the Intrinsic Dimension of Objective Landscapes
Chunyuan Li, Heerad Farkhoor, Rosanne Liu, and Jason Yosinski · 2018
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Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Daniel Soudry and Yair Carmon · 2016
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Critical learning periods in deep neural networks
Alessandro Achille, Matteo Rovere, and Stefano Soatto · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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The loss surface of deep and wide neural networks
Quynh Nguyen and Matthias Hein · 2017
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
M. Raghu, J. Gilmer, J. Yosinski, and J. Sohl-Dickstein · 2017
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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun
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Chen Xing, Devansh Arpit, Christos Tsirigotis, and Yoshua Bengio · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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A closer look at deep learning heuristics: Learning rate restarts, warmup and distillation
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