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Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research.
Deep learning: Methods and applications
Li Deng and Dong Yu · 1932
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Learning deep architectures for ai
Yoshua Bengio · 1935
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Comparing svm and convolutional networks for epileptic seizure prediction from intracranial eeg
Piotr Mirowski, Yann LeCun, Deepak Madhavan, and Ruben Kuzniecky · 2008
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A deep learning architecture comprising homogeneous cortical circuits for scalable spatiotemporal pattern inference
Itamar Arel, Derek Rose, and Tom Karnowski · 2009
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Research frontier: Deep machine learning–a new frontier in artificial intelligence research
Itamar Arel, Derek C. Rose, and Thomas P. Karnowski · 2010
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Discovering binary codes for documents by learning deep generative models
Geoffrey Hinton and Ruslan Salakhutdinov · 2010
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Bidirectional lstm networks for context-sensitive keyword detection in a cognitive virtual agent framework
Martin Wöllmer, Florian Eyben, Alex Graves, Björn Schuller, and Gerhard Rigoll · 2010
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Theano: A cpu and gpu math compiler in python
James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph P. Turian, David Warde-Farley, and Yoshua Bengio · 2011
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An overview of deep-structured learning for information processing
Li Deng · 2011
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On deep generative models with applications to recognition
M. Ranzato, J. Susskind, V. Mnih, and G. Hinton · 2011
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Deep neural networks for acoustic modeling in speech recognition
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, and Brian Kingsbury · 2012
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3d convolutional neural networks for human action recognition
Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Deep lambertian networks
Yichuan Tang, Ruslan Salakhutdinov, and Geoffrey Hinton · 2012
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Deep learning of representations: Looking forward
Yoshua Bengio · 2013
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Classifying and visualizing motion capture sequences using deep neural networks
Kyunghyun Cho and Xi Chen · 2013
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A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection , pages 403–410
Angel Alfonso Cruz-Roa, John Edison Arevalo Ovalle, Anant Madabhushi, and Fabio Augusto González Osorio · 2013
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Fast image scanning with deep max-pooling convolutional neural networks
Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca Maria Gambardella, and Jürgen Schmidhuber · 2013
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Maxout networks
Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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Deep learning for detecting robotic grasps
Ian Lenz, Honglak Lee, and Ashutosh Saxena · 2013
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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A fast learning algorithm for image segmentation with max-pooling convolutional networks
Jonathan Masci, Alessandro Giusti, Dan C. Ciresan, Gabriel Fricout, and Jürgen Schmidhuber · 2013
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Multi-scale pyramidal pooling network for generic steel defect classification
Jonathan Masci, Ueli Meier, Gabriel Fricout, and Jürgen Schmidhuber · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Calculating optimal jungling routes in dota2 using neural networks and genetic algorithms
Tom Batsford · 2014
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cudnn: Efficient primitives for deep learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B. Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom · 2014
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Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
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Convolutional neural networks for sentence classification
Yoon Kim · 2014
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Distributed representations of sentences and documents
Quoc V. Le and Tomas Mikolov · 2014
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Deepreid: Deep filter pairing neural network for person re-identification
Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune · 2014
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Learning and transferring mid-level image representations using convolutional neural networks
Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic · 2014
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CNN features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Deep Learning in Neural Networks: An Overview , volume abs/1404.7828
Jürgen Schmidhuber · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014
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Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
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Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan · 2014
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Jason Weston, Sumit Chopra, and Antoine Bordes · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Cited alongside, same era.
Deep speech 2: End-to-end speech recognition in english and mandarin
Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Tony Han, Awni Y. Hannun, Billy Jun, Patrick LeGresley, Libby Lin, Sharan Narang, Andrew Y. Ng, Sherjil Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Yi Wang, Zhiqian Wang, Chong Wang, Bo Xiao, Dani Yogatama, Jun Zhan, and Zhenyao Zhu · 2015
Cited alongside, same era.
Comparative study of caffe, neon, theano, and torch for deep learning
Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, and Mohak Shah · 2015
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Exploring the limits of language modeling
Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
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Eunhee Kang, Junhong Min, and Jong Chul Ye · 2016
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Zoneout: Regularizing rnns by randomly preserving hidden activations
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, and Chris Pal · 2016
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer · 2016
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Gated feedback recurrent neural networks
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio · 2015
Cited alongside, same era.
Deep generative image models using a laplacian pyramid of adversarial networks
Emily L. Denton, Soumith Chintala, Arthur Szlam, and Robert Fergus · 2015
Cited alongside, same era.
Ross B. Girshick · 2015
Cited alongside, same era.
Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, and Jürgen Schmidhuber · 2015
Cited alongside, same era.
Recent advances in convolutional neural networks
Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, and Gang Wang · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Cited alongside, same era.
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
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Multi-class generative adversarial networks with the L2 loss function
Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, and Zhen Wang · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Convolutional residual memory networks
Joel Moniz and Christopher J. Pal · 2016
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Attention and augmented recurrent neural networks
Chris Olah and Shan Carter · 2016
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Creating a universal snp and small indel variant caller with deep neural networks
Ryan Poplin, Dan Newburger, Jojo Dijamco, Nam Nguyen, Dion Loy, Sam S. Gross, Cory Y. McLean, and Mark A. DePristo · 2016
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One-shot generalization in deep generative models
Danilo Rezende, Shakir, Ivo Danihelka, Karol Gregor, and Daan Wierstra · 2016
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Improved techniques for training gans
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 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, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Resnet in resnet: Generalizing residual architectures
Sasha Targ, Diogo Almeida, and Kevin Lyman · 2016
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Texture networks: Feed-forward synthesis of textures and stylized images
Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor S. Lempitsky · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2016
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Dynamic memory networks for visual and textual question answering
Caiming Xiong, Stephen Merity, and Richard Socher · 2016
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Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A. Efros · 2016
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Deep voice: Real-time neural text-to-speech
Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Greg Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Jonathan Raiman, Shubho Sengupta, and Mohammad Shoeybi · 2017
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Pixelnet: Representation of the pixels, by the pixels, and for the pixels
Aayush Bansal, Xinlei Chen, Bryan C. Russell, Abhinav Gupta, and Deva Ramanan · 2017
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A neural parametric singing synthesizer
Merlijn Blaauw and Jordi Bonada · 2017
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
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Demystifying alphago zero as alphago GAN
Xiao Dong, Jiasong Wu, and Ling Zhou · 2017
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin · 2017
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A knowledge-grounded neural conversation model
Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, and Michel Galley · 2017
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Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Adversarial attacks on neural network policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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Batch renormalization: Towards reducing minibatch dependence in batch-normalized models
Sergey Ioffe · 2017
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Learning to discover cross-domain relations with generative adversarial networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, and Jiwon Kim · 2017
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Fader networks: Manipulating images by sliding attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, and Marc’Aurelio Ranzato · 2017
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ME R-CNN: multi-expert region-based CNN for object detection
Hyungtae Lee, Sungmin Eum, and Heesung Kwon · 2017
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Deep reinforcement learning: An overview
Yuxi Li · 2017
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Fujun Luan, Sylvain Paris, Eli Shechtman, and Kavita Bala · 2017
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Chainercv: a library for deep learning in computer vision
Yusuke Niitani, Toru Ogawa, Shunta Saito, and Masaki Saito · 2017
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A wavenet for speech denoising
Dario Rethage, Jordi Pons, and Xavier Serra · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton · 2017
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Samira Shabanian, Devansh Arpit, Adam Trischler, and Yoshua Bengio · 2017
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Honk: A pytorch reimplementation of convolutional neural networks for keyword spotting
Raphael Tang and Jimmy Lin · 2017
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Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2017
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Capsule network performance on complex data
Edgar Xi, Selina Bing, and Yang Jin · 2017
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Recent trends in deep learning based natural language processing
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria · 2017
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Deep learning for environmentally robust speech recognition: An overview of recent developments
Zixing Zhang, Jürgen T. Geiger, Jouni Pohjalainen, Amr El-Desoky Mousa, and Björn W. Schuller · 2017
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Deep learning in remote sensing: a review
Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, and Friedrich Fraundorfer · 2017
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Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, and Jürgen Schmidhuber · 2017
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Generating neural networks with neural networks
Lior Deutsch · 2018
Closest in time.
Deep learning: A critical appraisal
Gary Marcus · 2018
Closest in time.