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Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task.
On information and sufficiency
Solomon Kullback and Richard A Leibler · 1951
Earlier work this paper cites.
Learning representations by back propagating errors
David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams · 1986
Earlier work this paper cites.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
Bayesian back-propagation
Wray L Buntine and Andreas S Weigend · 1991
Earlier work this paper cites.
Transforming neural-net output levels to probability distributions
John S Denker and Yann LeCu · 1991
Earlier work this paper cites.
A practical bayesian framework for backprop networks
David J C Mackay · 1991
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
Geoffrey E Hinton and Drew Van Camp · 1993
Earlier work this paper cites.
Simplifying neural nets by discovering flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1995
Earlier work this paper cites.
Probable networks and plausible predictions—a review of practical bayesian methods for supervised neural networks
David JC MacKay · 1995
Earlier work this paper cites.
Hyperparameters: optimize, or integrate out?
David JC MacKay · 1996
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
Ensemble learning in bayesian neural networks
David Barber and Christopher M Bishop · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
A view of the em algorithm that justifies incremental, sparse, and other variants
Radford M Neal and Geoffrey E Hinton · 1998
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando CN Pereira · 2001
Earlier work this paper cites.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
D. Martin, C. Fowlkes, D. Tal, and J. Malik · 2001
Earlier work this paper cites.
Eigenface-domain super-resolution for face recognition
B. K. Gunturk, A. U. Batur, Y. Altunbasak, M. H. Hayes, and R. M. Mersereau · 2003
Earlier work this paper cites.
Constructing free-energy approximations and generalized belief propagation algorithms
Jonathan S Yedidia, William T Freeman, and Yair Weiss · 2005
Earlier work this paper cites.
Variational free energy and the laplace approximation
Karl Friston, Jérémie Mattout, Nelson Trujillo-Barreto, John Ashburner, and Will Penny · 2007
Earlier work this paper cites.
80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T. Freeman · 2008
Earlier work this paper cites.
Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Cited alongside, same era.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Cited alongside, same era.
Practical variational inference for neural networks
Alex Graves · 2011
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Dropout as a Bayesian approximation: Insights and applications
Yarin Gal and Zoubin Ghahramani · 2015
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Song Han, Huizi Mao, and William J. Dally · 2015
Later among the works it cites.
Variational dropout and the local reparameterization trick
Diederik P Kingma, Tim Salimans, and Max Welling · 2015
Later among the works it cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Later among the works it cites.
Learning the number of neurons in deep networks
Jose M. Alvarez and Mathieu Salzmann · 2016
Later among the works it cites.
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Cited alongside, same era.
Bayesian learning for neural networks
Radford M Neal · 2012
Cited alongside, same era.
Cardiac image super-resolution with global correspondence using multi-atlas patchmatch
Wenzhe Shi, Jose Caballero, Christian Ledig, Xiahai Zhuang, Wenjia Bai, Kanwal Bhatia, Antonio M. Simoes Monteiro de Marvao, Tim Dawes, Declan O’Regan, and Daniel Rueckert · 2013
Cited alongside, same era.
Fast dropout training
Sida Wang and Christopher Manning · 2013
Cited alongside, same era.
Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
Cited alongside, same era.
Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir D. Bourdev · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Alex Graves · 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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Curiosity-driven exploration in deep reinforcement learning via bayesian neural networks
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2016
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Neural Networks 1
Karparthy, Andrej · 2016
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Efficient exploration for dialogue policy learning with bbq networks & replay buffer spiking
Zachary C Lipton, Jianfeng Gao, Lihong Li, Xiujun Li, Faisal Ahmed, and Li Deng · 2016
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Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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The power of sparsity in convolutional neural networks
Soravit Changpinyo, Mark Sandler, and Andrey Zhmoginov · 2017
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Bayesian recurrent neural networks
Meire Fortunato, Charles Blundell, and Oriol Vinyals · 2017
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chapter18_variational-methods-and-uncertainty
Gluon MXnet · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
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Exploring sparsity in recurrent neural networks
Sharan Narang, Gregory F. Diamos, Shubho Sengupta, and Erich Elsen · 2017
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Reliable uncertainty estimates in deep neural networks using noise contrastive priors
Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, and James Davidson · 2018
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Uncertainty quantification using bayesian neural networks in classification: Application to ischemic stroke lesion segmentation
Yongchan Kwon, Joong-Ho Won, Beom Joon Kim, and Myunghee Cho Paik · 2018
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Variance networks: When expectation does not meet your expectations
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2018
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Bayesian convolutional neural networks with variational inference
Kumar Shridhar, Felix Laumann, Adrian Llopart Maurin, Martin Olsen, and Marcus Liwicki · 2018
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