Fetching the paper…
Reading the bibliography…
Deep neural networks are able to solve tasks across a variety of domains and modalities of data.
Interpreting neural network connection weights
David G Garson · 1991
Earlier work this paper cites.
Data mining of inputs: analysing magnitude and functional measures
Tamás D Gedeon · 1997
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin · 2000
Earlier work this paper cites.
Assessing the impact of input features in a feedforward neural network
Wenjia Wang, Phillis Jones, and Derek Partridge · 2000
Earlier work this paper cites.
Numeric sensitivity analysis applied to feedforward neural networks
JJ Montano and A Palmer · 2003
Earlier work this paper cites.
Slow feature analysis yields a rich repertoire of complex cell properties
Pietro Berkes and Laurenz Wiskott · 2005
Earlier work this paper cites.
Two-way interaction of input variables in the sensitivity analysis of neural network models
Muriel Gevrey, Ioannis Dimopoulos, and Sovan Lek · 2006
Earlier work this paper cites.
Limitations of sensitivity analysis for neural networks in cases with dependent inputs
Maciej A Mazurowski and Przemyslaw M Szecowka · 2006
Earlier work this paper cites.
Explaining classifications for individual instances
Marko Robnik-Šikonja and Igor Kononenko · 2008
Earlier work this paper cites.
Artificial intelligence as a positive and negative factor in global risk
Eliezer Yudkowsky · 2008
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
Earlier work this paper cites.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÞller · 2010
Earlier work this paper cites.
Recurrent neural network based language model
Tomas Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, and Sanjeev Khudanpur · 2010
Earlier work this paper cites.
Adaptive deconvolutional networks for mid and high level feature learning
Matthew D Zeiler, Graham W Taylor, and Rob Fergus · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Cited alongside, same era.
Rectifier nonlinearities improve neural network acoustic models
Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng · 2013
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Later among the works it cites.
Grammar as a foreign language
Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey E. Hinton · 2015
Later among the works it cites.
Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
Later among the works it cites.
Visualizing and Understanding Convolutional Networks
Matthew Zeiler and Rob Fergus · 2015
Later among the works it cites.
Towards transparent ai systems: Interpreting visual question answering models
Yash Goyal, Akrit Mohapatra, Devi Parikh, and Dhruv Batra · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Recurrent models of visual attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, et al · 2014
Cited alongside, same era.
Describing multimedia content using attention-based encoder-decoder networks
Kyunghyun Cho, Aaron Courville, and Yoshua Bengio · 2015
Cited alongside, same era.
Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra · 2015
Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
Cited alongside, same era.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan L. Boyd-Graber, and Hal Daumé · 2015
Cited alongside, same era.
Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher · 2016
Later among the works it cites.
Rationalizing Neural Predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
Later among the works it cites.
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune · 2016
Later among the works it cites.
Control of memory, active perception, and action in minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder P. Singh, and Honglak Lee · 2016
Later among the works it cites.
Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Later among the works it cites.
Stacked attention networks for image question answering
Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola · 2016
Later among the works it cites.
Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
Closest in time.