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DateTitleAuthorsLinkRead order
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From the Deep Learning Papers Reading Roadmap
https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
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5/28/2015Deep learningYann LeCun, Yoshua Bengio, Geoffrey Hinton
http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf
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6/8/2006A fast learning algorithm for deep belief netsGeoffrey Hinton, Simon Osindero, Yee-Whye Teh
http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf
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6/28/2006Reducing the dimensionality of data with neural networksGeoffrey Hinton, Ruslan R. Salakhutdinov
http://www.cs.toronto.edu/~hinton/science.pdf
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12/3/2012Imagenet classification with deep convolutional neural networksAlex Krizhevsky, Ilya Sutskever, Geoffrey Hinton
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
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9/4/2014Very deep convolutional networks for large-scale image recognitionKaren Simonyan, Andrew Zissermanhttps://arxiv.org/abs/1409.1556v65
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12/16/2013Network In NetworkMin Lin, Qiang Chen, Shuicheng Yanhttps://arxiv.org/abs/1312.44006
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9/17/2014Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
https://arxiv.org/abs/1409.4842v17
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12/10/2015Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
https://arxiv.org/abs/1512.03385v1
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10/15/2012
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, et al.
http://cs224d.stanford.edu/papers/maas_paper.pdf
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3/22/2013Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, Geoffrey Hinton
https://arxiv.org/abs/1303.5778v110
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6/21/2014Towards End-To-End Speech Recognition with Recurrent Neural NetworksAlex Graves, Navdeep Jaitly
http://www.jmlr.org/proceedings/papers/v32/graves14.pdf
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7/24/2015
Fast and accurate recurrent neural network acoustic models for speech recognition
Haşim Sak, et al.
https://arxiv.org/abs/1507.06947v1
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12/8/2015Deep speech 2: End-to-end speech recognition in english and mandarinDario Amodei, et al.
https://arxiv.org/abs/1512.02595v1
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10/17/2016Achieving Human Parity in Conversational Speech Recognition
W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig
https://arxiv.org/abs/1610.05256v2
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7/3/2012
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
https://arxiv.org/abs/1207.0580v115
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6/1/2014Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
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2/11/2015
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe, Christian Szegedy
https://arxiv.org/abs/1502.03167v3
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7/21/2016Layer normalizationJimmy Lei Ba, Jamie Ryan Kiros, Geoffrey Hinton
https://arxiv.org/abs/1607.06450v1
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2/9/2016
Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
https://arxiv.org/abs/1602.02830v3
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8/18/2016Decoupled neural interfaces using synthetic gradients
Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero, Oriol Vinyals, Alex Graves, David Silver, Koray Kavukcuoglu
https://arxiv.org/abs/1608.05343v2
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11/18/2015Net2net: Accelerating learning via knowledge transferTianqi Chen, Ian Goodfellow, Jonathon Shlens
https://arxiv.org/abs/1511.05641v4
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3/5/2016Network Morphism
Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen
https://arxiv.org/abs/1603.01670v2
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6/17/2013On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, Geoffrey Hinton
http://www.jmlr.org/proceedings/papers/v28/sutskever13.pdf
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12/22/2014Adam: A method for stochastic optimizationDiederik Kingma, Jimmy Ba.https://arxiv.org/abs/1412.6980v924
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6/14/2016Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas
https://arxiv.org/abs/1606.04474v2
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10/1/2015
Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, William J. Dally
https://arxiv.org/abs/1510.00149v5
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2/24/2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
https://arxiv.org/abs/1602.07360v4
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12/29/2011Building high-level features using large scale unsupervised learning
Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeff Dean, Andrew Y. Ng
https://arxiv.org/abs/1112.6209v528
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12/20/2013Auto-encoding variational bayesDiederik Kingma, Max Welling
https://arxiv.org/abs/1312.6114v10
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6/10/2014Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
https://arxiv.org/abs/1406.2661v130
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11/19/2015
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala
https://arxiv.org/abs/1511.06434v2
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2/16/2015DRAW: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra
https://arxiv.org/abs/1502.04623v2
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1/25/2016Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu
https://arxiv.org/abs/1601.06759v3
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6/16/2016Conditional image generation with PixelCNN decoders
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu
https://arxiv.org/abs/1606.05328v2
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8/4/2013Generating sequences with recurrent neural networksAlex Graveshttps://arxiv.org/abs/1308.0850v535
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6/3/2014
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
https://arxiv.org/abs/1406.1078v336
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9/10/2014Sequence to sequence learning with neural networksIlya Sutskever, Oriol Vinyals, Quoc V. Lehttps://arxiv.org/abs/1409.3215v337
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9/1/2014Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
https://arxiv.org/abs/1409.0473v738
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6/19/2015A neural conversational modelOriol Vinyals, Quoc Le
https://arxiv.org/abs/1506.05869v3
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10/20/2014Neural turing machinesAlex Graves, Greg Wayne, Ivo Danihelkahttps://arxiv.org/abs/1410.5401v240
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5/4/2015Reinforcement Learning Neural Turing MachinesWojciech Zaremba, Ilya Sutskeverhttp://arxiv.org/abs/1505.00521v341
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10/15/2014Memory networksJason Weston, Sumit Chopra, Antoine Bordeshttp://arxiv.org/abs/1410.3916v1142
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3/31/2015End-to-end memory networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus
http://arxiv.org/abs/1503.08895v543
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6/9/2015Pointer networksOriol Vinyals, Meire Fortunato, Navdeep Jaitlyhttp://arxiv.org/abs/1506.03134v244
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10/27/2016Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, Demis Hassabis
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwiymei-v6_gAhVGilQKHearAmUQFjAAegQIChAB&url=https%3A%2F%2Fwww.nature.com%2Farticles%2Fnature20101&usg=AOvVaw2IdzkvQUIO84KRHmXHhgAe
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12/19/2013Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
https://arxiv.org/abs/1312.5602v146
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2/26/2015Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=2ahUKEwiw4PaJwK_gAhWmiVQKHYLiAV0QFjAAegQICRAC&url=https%3A%2F%2Fweb.stanford.edu%2Fclass%2Fpsych209%2FReadings%2FMnihEtAlHassibis15NatureControlDeepRL.pdf&usg=AOvVaw0uqHxqo8Yyn3cmySQWqe8Z
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11/20/2015Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
http://arxiv.org/abs/1511.06581v348
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2/4/2016Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu
http://arxiv.org/abs/1602.01783v249
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9/9/2015Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
http://arxiv.org/abs/1509.02971v550
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3/2/2016Continuous Deep Q-Learning with Model-based Acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine
http://arxiv.org/abs/1603.00748v151
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2/19/2015Trust region policy optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel
http://arxiv.org/abs/1502.05477v552
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1/1/2016Mastering the game of Go with deep neural networks and tree search
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis
https://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf
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7/2/2011Deep Learning of Representations for Unsupervised and Transfer LearningYoshua Bengio
http://www.jmlr.org/proceedings/papers/v27/bengio12a/bengio12a.pdf
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3/15/2013Lifelong Machine Learning Systems: Beyond Learning AlgorithmsDaniel L. Silver, Qiang Yang, Lianghao Li
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.7800&rep=rep1&type=pdf
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3/9/3015Distilling the knowledge in a neural networkGeoffrey Hinton, Oriol Vinyals, Jeff Dean
https://arxiv.org/abs/1503.02531v1
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11/19/2015Policy distillation
Andrei A. Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell
http://arxiv.org/abs/1511.06295v257
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11/29/2015Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
http://arxiv.org/abs/1511.06342v458
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6/15/2016Progressive neural networks
Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, Raia Hadsell
http://arxiv.org/abs/1606.04671v359
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12/11/2015Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
http://clm.utexas.edu/compjclub/wp-content/uploads/2016/02/lake2015.pdf
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6/6/2015Siamese Neural Networks for One-shot Image Recognition
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov
http://www.cs.utoronto.ca/~gkoch/files/msc-thesis.pdf
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5/19/2016One-shot Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap
http://arxiv.org/abs/1605.06065v162
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6/13/2016Matching Networks for One Shot Learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
http://arxiv.org/abs/1606.04080v263
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6/9/2016Low-shot Visual Recognition by Shrinking and Hallucinating FeaturesBharath Hariharan, Ross Girshickhttp://arxiv.org/abs/1606.02819v464
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4/21/2012
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Antoine Bordes, Xavier Glorot, Jason Weston, Yoshua Bengio
https://www.hds.utc.fr/~bordesan/dokuwiki/lib/exe/fetch.php?id=en%3Apubli&cache=cache&media=en:bordes12aistats.pdf
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10/16/2013
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean
http://arxiv.org/abs/1310.4546v166
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6/24/2015
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, Richard Socher
http://arxiv.org/abs/1506.07285v567
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7/26/2015Character-Aware Neural Language Models
Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush
http://arxiv.org/abs/1508.06615v468
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2/19/2015Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov
http://arxiv.org/abs/1502.05698v10
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6/10/2015Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
http://arxiv.org/abs/1506.03340v370
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6/6/2016Very Deep Convolutional Networks for Text Classification
Alexis Conneau, Holger Schwenk, Loïc Barrault, Yann Lecun
http://arxiv.org/abs/1606.01781v271
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6/6/2016Bag of Tricks for Efficient Text Classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
http://arxiv.org/abs/1607.01759v372
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12/5/2013Deep neural networks for object detection
Christian Szegedy, Alexander Toshev, Dumitru Erhan
http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf
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11/11/2013
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
http://arxiv.org/abs/1311.2524v574
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6/18/2014
Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
http://arxiv.org/abs/1406.4729v475
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4/30/2015Fast R-CNNRoss Girshickhttp://arxiv.org/abs/1504.08083v276
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6/4/2015
Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sunhttp://arxiv.org/abs/1506.01497v377
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6/8/2015You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
http://arxiv.org/abs/1506.02640v578
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12/8/2015SSD: Single Shot MultiBox Detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg
http://arxiv.org/abs/1512.02325v579
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5/20/2016R-FCN: Object Detection via Region-based Fully Convolutional NetworksJifeng Dai, Yi Li, Kaiming He, Jian Sunhttp://arxiv.org/abs/1605.06409v280
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11/14/2014Fully convolutional networks for semantic segmentationJonathan Long, Evan Shelhamer, Trevor Darrellhttp://arxiv.org/abs/1411.4038v281
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12/22/2014
Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
http://arxiv.org/abs/1412.7062v482
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6/20/2015Learning to segment object candidatesPedro O. Pinheiro, Ronan Collobert, Piotr Dollarhttp://arxiv.org/abs/1506.06204v283
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12/14/2015Instance-aware semantic segmentation via multi-task network cascadesJifeng Dai, Kaiming He, Jian Sunhttp://arxiv.org/abs/1512.04412v184
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3/29/2016Instance-sensitive Fully Convolutional Networks
Jifeng Dai, Kaiming He, Yi Li, Shaoqing Ren, Jian Sun
http://arxiv.org/abs/1603.08678v185
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3/20/2017Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick
http://arxiv.org/abs/1703.06870v386
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12/5/2013Learning a deep compact image representation for visual trackingNaiyan Wang, Dit-Yan Yeung
http://papers.nips.cc/paper/5192-learning-a-deep-compact-image-representation-for-visual-tracking.pdf
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1/19/2015Transferring rich feature hierarchies for robust visual trackingNaiyan Wang, Siyi Li, Abhinav Gupta, Dit-Yan Yeunghttp://arxiv.org/abs/1501.04587v288
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12/1/2015Visual tracking with fully convolutional networksLijun Wang, et al.
http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Wang_Visual_Tracking_With_ICCV_2015_paper.pdf
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4/6/2016Learning to Track at 100 FPS with Deep Regression NetworksDavid Held, Sebastian Thrun, Silvio Savaresehttp://arxiv.org/abs/1604.01802v290
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6/30/2016Fully-Convolutional Siamese Networks for Object Tracking
Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr
http://arxiv.org/abs/1606.09549v291
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7/12/2016
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Martin Danelljan, Andreas Robinson, Fahad Shahbaz Khan, Michael Felsberg
http://arxiv.org/abs/1608.03773v292
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7/25/2016Modeling and Propagating CNNs in a Tree Structure for Visual TrackingHyeonseob Nam, Mooyeol Baek, Bohyung Hanhttp://arxiv.org/abs/1608.07242v193
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11/15/2010Every picture tells a story: Generating sentences from imagesAli Farhadi, et al.
https://www.cs.cmu.edu/~afarhadi/papers/sentence.pdf
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6/20/2011Baby talk: Understanding and generating image descriptionsGirish Kulkarni, et al.
http://tamaraberg.com/papers/generation_cvpr11.pdf
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11/17/2014Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan
http://arxiv.org/abs/1411.4555v296
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11/17/2014
Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, Trevor Darrell
http://arxiv.org/abs/1411.4389v497
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