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At the heart of deep learning we aim to use neural networks as function approximators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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Tangent prop-a formalism for specifying selected invariances in an adaptive network
Patrice Simard, Bernard Victorri, Yann LeCun, and John S Denker · 1991
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On learning the derivatives of an unknown mapping with multilayer feedforward networks
A Ronald Gallant and Halbert White · 1992
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Approximate dynamic programming for real-time control and neural modeling
Paul J Werbos · 1992
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Neural networks for control
W Thomas Miller, Paul J Werbos, and Richard S Sutton · 1995
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Actor-critic algorithms
Vijay R Konda and John N Tsitsiklis · 1999
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Estimation of non-normalized statistical models using score matching
Aapo Hyvärinen · 2005
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Least squares solutions of the hjb equation with neural network value-function approximators
Yuval Tassa and Tom Erez · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Higher order contractive auto-encoder
Salah Rifai, Grégoire Mesnil, Pascal Vincent, Xavier Muller, Yoshua Bengio, Yann Dauphin, and Xavier Glorot · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Value-gradient learning
Michael Fairbank and Eduardo Alonso · 2012
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Simple and fast calculation of the second-order gradients for globalized dual heuristic dynamic programming in neural networks
Michael Fairbank, Eduardo Alonso, and Danil Prokhorov · 2012
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Handbook of complex variables
Steven G Krantz · 2012
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Decoupled neural interfaces using synthetic gradients
Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Deep model compression: Distilling knowledge from noisy teachers
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Razvan Pascanu and Yoshua Bengio · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
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Bharat Bhusan Sau and Vineeth N Balasubramanian · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Wavenet: A generative model for raw audio
Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Synthetic gradient methods with virtual forward-backward networks
Shin-ichi Maeda Koyama Masanori Takeru Miyato, Daisuke Okanohara · 2017
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Exploiting gradients and hessians in bayesian optimization and bayesian quadrature
Anqi Wu, Mikio C Aoi, and Jonathan W Pillow · 2017
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