https://github.com/abhshkdz/papers

:paperclip: Summaries of papers on deep learning
https://github.com/abhshkdz/papers

artificial-intelligence computer-vision deep-learning deep-neural-networks machine-learning

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:paperclip: Summaries of papers on deep learning

• Host: GitHub
• URL: https://github.com/abhshkdz/papers
• Owner: abhshkdz
• Created: 2016-01-27T10:39:24.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2019-10-13T06:14:17.000Z (about 5 years ago)
• Last Synced: 2024-08-03T23:27:54.025Z (5 months ago)
• Topics: artificial-intelligence, computer-vision, deep-learning, deep-neural-networks, machine-learning
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• Forks: 124
• Open Issues: 1
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  • Readme: README.md

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README

        
Summaries of papers on deep learning.

2018

- World Models [[Paper](https://arxiv.org/abs/1803.10122)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/world-models.md)]

    - David Ha, Jürgen Schmidhuber, ArXiv, 2018

2017

- A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment [[Paper](https://arxiv.org/abs/1703.09831)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/a-deep-compositional-framework-for-human-like-language-acquisition-in-virtual-environment.md)]

    - Haonan Yu, Haichao Zhang, Wei Xu, ArXiv, 2017

- A simple neural network module for relational reasoning [[Paper](https://arxiv.org/abs/1706.01427)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/a-simple-neural-network-module-for-relational-reasoning.md)]

    - Adam Santoro, David Raposo, David G.T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap, NIPS, 2017

- Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning [[Paper](https://arxiv.org/abs/1711.07613)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/are-you-talking-to-me-reasoned-visual-dialog-generation-through-adversarial-learning.md)]

    - Qi Wu, Peng Wang, Chunhua Shen, Ian Reid, Anton van den Hengel, ArXiv, 2017

- From Red Wine to Red Tomato: Composition with Context [[Paper](http://www.cs.cmu.edu/~imisra/data/composing_cvpr17.pdf)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/from-red-wine-to-red-tomato-composition-with-context.md)]

    - Ishan Misra, Abhinav Gupta, Martial Hebert, CVPR, 2017

- Towards Diverse and Natural Image Descriptions via a Conditional GAN [[Paper](https://arxiv.org/abs/1703.06029)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/towards-diverse-and-natural-image-descriptions-via-a-conditional-gan.md)]

    - Bo Dai, Sanja Fidler, Raquel Urtasun, Dahua Lin, ICCV, 2017

2016

- Actions ~ Transformations [[Paper](https://arxiv.org/abs/1512.00795)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/actions-~-transformations.md)]

    - Xiaolong Wang, Ali Farhadi, Abhinav Gupta, CVPR, 2016

- Building Machines That Learn and Think Like People [[Paper](https://arxiv.org/abs/1604.00289)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/building-machines-that-learn-and-think-like-people.md)]

    - Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman, Behavioral and Brain Sciences, 2016

- Deep Compositional Question Answering with Neural Module Networks [[Paper](http://arxiv.org/abs/1511.02799)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/deep-compositional-question-answering-with-neural-module-networks.md)]

    - Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, CVPR, 2016

- Deep Networks with Stochastic Depth [[Paper](https://arxiv.org/abs/1603.09382)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/deep-networks-with-stochastic-depth.md)]

    - Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger, ArXiv, 2016

- Deep Reinforcement Learning for Dialogue Generation [[Paper](https://arxiv.org/abs/1606.01541)] [[Review](reviews/deep-reinforcement-learning-for-dialogue-generation.md)]

    - Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky, ArXiv, 2016

- Deep Residual Learning for Image Recognition [[Paper](http://arxiv.org/abs/1512.03385)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/deep-residual-learning-for-image-recognition.md)]

    - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, ArXiv, 2016

- Delving Deeper into Convolutional Networks for Learning Video Representations [[Paper](http://arxiv.org/abs/1511.06432)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/delving-deeper-into-convolutional-networks-for-learning-video-representations.md)]

    - Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville, ICLR, 2016

- Dynamic Capacity Networks [[Paper](http://arxiv.org/abs/1511.07838)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/dynamic-capacity-networks.md)]

    - Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville, ICML, 2016

- Identity Mappings in Deep Residual Networks [[Paper](http://arxiv.org/abs/1603.05027)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/identity-mappings-in-deep-residual-networks.md)]

    - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, ArXiv, 2016

- Net2Net: Accelerating Learning via Knowledge Transfer [[Paper](http://arxiv.org/abs/1511.05641)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/net2net-accelerating-learning-via-knowledge-transfer.md)]

    - Tianqi Chen, Ian Goodfellow, Jonathon Shlens, ICLR, 2016

- Perceptual Losses for Real-Time Style Transfer and Super-Resolution [[Paper](https://arxiv.org/abs/1603.08155)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/perceptual-losses-for-real-time-style-transfer-and-super-resolution.md)]

    - Justin Johnson, Alexandre Alahi, Li Fei-Fei, ArXiv, 2016

- Recurrent Batch Normalization [[Paper](http://arxiv.org/abs/1603.09025)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/recurrent-batch-normalization.md)]

    - Tim Cooijmans, Nicolas Ballas, César Laurent, Aaron Courville, ArXiv, 2016

- Residual Networks are Exponential Ensembles of Relatively Shallow Networks [[Paper](http://arxiv.org/abs/1605.06431)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/residual-networks-are-exponential-ensembles-of-relatively-shallow-networks.md)]

    - Andreas Veit, Michael Wilber, Serge Belongie, ArXiv, 2016

- Residual Networks of Residual Networks: Multilevel Residual Networks, ArXiv, 2016 [[Paper](http://arxiv.org/abs/1608.02908)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/residual-networks-of-residual-networks-multilevel-residual-networks.md)]

    - Ke Zhang, Miao Sun, Tony X. Han, Xingfang Yuan, Liru Guo, Tao Liu, ArXiv, 2016

2015

- Deep Visual Analogy-Making [[Paper](https://papers.nips.cc/paper/5845-deep-visual-analogy-making)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/deep-visual-analogy-making.md)]

    - Scott E. Reed, Yi Zhang, Yuting Zhang, Honglak Lee, NIPS, 2015

- DenseCap: Fully Convolutional Localization Networks for Dense Captioning [[Paper](http://arxiv.org/abs/1511.07571)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/densecap-fully-convolutional-localization-networks-for-dense-captioning.md)]

    - Justin Johnson, Andrej Karpathy, Li Fei-Fei, ArXiv, 2015

- DRAW: A Recurrent Neural Network For Image Generation [[Paper](http://arxiv.org/abs/1502.04623)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/draw-a-recurrent-neural-network-for-image-generation.md)]

    - Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, ICML, 2015

- Neural Machine Translation by Jointly Learning to Align and Translate [[Paper](http://arxiv.org/abs/1409.0473)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/neural-machine-translation-by-jointly-learning-to-align-and-translate.md)]

    - Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, ICLR, 2015

- Object Detectors Emerge in Deep Scene CNNs [[Paper](http://arxiv.org/abs/1412.6856)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/object-detectors-emerge-in-deep-scene-cnns.md)]

    - Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, ICLR, 2015

- Spatial Transformer Networks [[Paper](http://arxiv.org/abs/1506.02025)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/spatial-transformer-networks.md)]

    - Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu, NIPS, 2015

- Stacked Attention Networks for Image Question Answering [[Paper](http://arxiv.org/abs/1511.02274)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/stacked-attention-networks-for-image-question-answering.md)]

    - Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, ArXiv, 2015

- Striving for Simplicity: the All Convolutional Net [[Paper](http://arxiv.org/abs/1412.6806)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/all-convolutional-net.md)]

    - Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller, ICLR, 2015

- You Only Look Once: Unified, Real-Time Object Detection [[Paper](http://arxiv.org/abs/1506.02640)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/you-only-look-once-unified-real-time-object-detection.md)]

    - Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, ArXiv15

2014

- Convolutional Neural Networks for Sentence Classification [[Paper](http://arxiv.org/abs/1408.5882)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/convolutional-neural-networks-for-sentence-classification.md)]

    - Yoon Kim, EMNLP, 2014

- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps [[Paper](http://arxiv.org/abs/1312.6034)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/deep-inside-convolutional-networks.md)]

    - Karen Simonyan, Andrea Vedaldi, Andrew Zisserman, ICLR, 2014

- Going Deeper with Convolutions [[Paper](http://arxiv.org/abs/1409.4842)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/going-deeper-with-convolutions.md)]

    - Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, ArXiv, 2014

- How transferable are features in deep neural networks? [[Paper](http://arxiv.org/abs/1411.1792)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/how-transferable-are-features-in-deep-neural-networks.md)]

    - Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson, NIPS, 2014

- Intriguing Properties of Neural Networks [[Paper](http://arxiv.org/abs/1312.6199)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/intriguing-properties-of-neural-networks.md)]

    - Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus, ICLR, 2014

- Learning Deep Features for Scene Recognition using Places Database [[Paper](http://places.csail.mit.edu/places_NIPS14.pdf)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/learning-deep-features-for-scene-recognition-using-places-database.md)]

    - Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva, NIPS, 2014

- Network in Network [[Paper](http://arxiv.org/abs/1312.4400)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/network-in-network.md)]

    - Min Lin, Qiang Chen, Shuicheng Yan, ICLR, 2014

- Neural Turing Machines [[Paper](https://arxiv.org/abs/1410.5401)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/neural-turing-machines.md)]

    - Alex Graves, Greg Wayne, Ivo Danihelka, ArXiv, 2014

- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation [[Paper](http://arxiv.org/abs/1311.2524)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/rich-feature-hierarchies-for-accurate-object-detection-and-semantic-segmentation.md)]

    - Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, CVPR, 2014

- Sequence to Sequence Learning with Neural Networks [[Paper](http://arxiv.org/abs/1409.3215)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/sequence-to-sequence-learning-with-neural-networks.md)]

    - Ilya Sutskever, Oriol Vinyals, Quoc V. Le, NIPS, 2014

- Very Deep Convolutional Networks for Large-Scale Image Recognition [[Paper](http://arxiv.org/abs/1409.1556)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/very-deep-convolutional-networks-for-large-scale-image-recognition.md)]

    - Karen Simonyan, Andrew Zisserman, ArXiv, 2014

- Visualizing and Understanding Convolutional Networks [[Paper](http://arxiv.org/abs/1311.2901)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/visualizing-and-understanding-convolutional-networks.md)]

    - Matthew D Zeiler, Rob Fergus, ECCV, 2014

2012

- ImageNet Classification with Deep Convolutional Neural Networks [[Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/imagenet-classification-with-deep-convolutional-neural-networks.md)]

    - Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton, NIPS, 2012

- What Question Would Turing Pose Today? [[Paper](http://www.aaai.org/ojs/index.php/aimagazine/article/view/2441)] [[Review](https://github.com/abhshkdz/papers/blob/master/reviews/what-question-would-turing-pose-today.md)]

    - Barbara Grosz, AI Magazine, 2012