Summaries of scientific papers on (not just) deep learning methods for NLP tasks!
Due to a fear of the blank page, this is built on top of the existing paper summaries by Das 🙏
2020
- Computationally Efficient NER Taggers with Combined Embeddings and Constrained Decoding [Paper] [Review]
- Brian Lester, Daniel Pressel, Amy Hemmeter, and Sagnik Ray Choudhury, Arxiv, 2020
2019
- TODO: Optimal Transport-based Alignment of Learned Character Representations for String Similarity [Paper] [Review]
- Derek Tam, Nicholas Monath, Ari Kobren, Aaron Traylor, Rajarshi Das, Andrew McCallum, ACL 2019 Florence
2018
2017
- A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment [Paper] [Review]
- Haonan Yu, Haichao Zhang, Wei Xu, ArXiv, 2017
- A simple neural network module for relational reasoning [Paper] [Review]
- 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] [Review]
- Qi Wu, Peng Wang, Chunhua Shen, Ian Reid, Anton van den Hengel, ArXiv, 2017
- From Red Wine to Red Tomato: Composition with Context [Paper] [Review]
- Ishan Misra, Abhinav Gupta, Martial Hebert, CVPR, 2017
- Towards Diverse and Natural Image Descriptions via a Conditional GAN [Paper] [Review]
- Bo Dai, Sanja Fidler, Raquel Urtasun, Dahua Lin, ICCV, 2017
2016
- Actions ~ Transformations [Paper] [Review]
- Xiaolong Wang, Ali Farhadi, Abhinav Gupta, CVPR, 2016
- Building Machines That Learn and Think Like People [Paper] [Review]
- 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] [Review]
- Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, CVPR, 2016
- Deep Networks with Stochastic Depth [Paper] [Review]
- Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Weinberger, ArXiv, 2016
- Deep Reinforcement Learning for Dialogue Generation [Paper] [Review]
- Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky, ArXiv, 2016
- Deep Residual Learning for Image Recognition [Paper] [Review]
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, ArXiv, 2016
- Delving Deeper into Convolutional Networks for Learning Video Representations [Paper] [Review]
- Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville, ICLR, 2016
- Dynamic Capacity Networks [Paper] [Review]
- Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville, ICML, 2016
- Identity Mappings in Deep Residual Networks [Paper] [Review]
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, ArXiv, 2016
- Net2Net: Accelerating Learning via Knowledge Transfer [Paper] [Review]
- Tianqi Chen, Ian Goodfellow, Jonathon Shlens, ICLR, 2016
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution [Paper] [Review]
- Justin Johnson, Alexandre Alahi, Li Fei-Fei, ArXiv, 2016
- Recurrent Batch Normalization [Paper] [Review]
- Tim Cooijmans, Nicolas Ballas, César Laurent, Aaron Courville, ArXiv, 2016
- Residual Networks are Exponential Ensembles of Relatively Shallow Networks [Paper] [Review]
- Andreas Veit, Michael Wilber, Serge Belongie, ArXiv, 2016
- Residual Networks of Residual Networks: Multilevel Residual Networks, ArXiv, 2016 [Paper] [Review]
- Ke Zhang, Miao Sun, Tony X. Han, Xingfang Yuan, Liru Guo, Tao Liu, ArXiv, 2016
2015
- Deep Visual Analogy-Making [Paper] [Review]
- Scott E. Reed, Yi Zhang, Yuting Zhang, Honglak Lee, NIPS, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning [Paper] [Review]
- Justin Johnson, Andrej Karpathy, Li Fei-Fei, ArXiv, 2015
- DRAW: A Recurrent Neural Network For Image Generation [Paper] [Review]
- Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, ICML, 2015
- Neural Machine Translation by Jointly Learning to Align and Translate [Paper] [Review]
- Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, ICLR, 2015
- Object Detectors Emerge in Deep Scene CNNs [Paper] [Review]
- Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, ICLR, 2015
- Spatial Transformer Networks [Paper] [Review]
- Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu, NIPS, 2015
- Stacked Attention Networks for Image Question Answering [Paper] [Review]
- Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, ArXiv, 2015
- Striving for Simplicity: the All Convolutional Net [Paper] [Review]
- Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller, ICLR, 2015
- You Only Look Once: Unified, Real-Time Object Detection [Paper] [Review]
- Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, ArXiv15
2014
- Convolutional Neural Networks for Sentence Classification [Paper] [Review]
- Yoon Kim, EMNLP, 2014
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps [Paper] [Review]
- Karen Simonyan, Andrea Vedaldi, Andrew Zisserman, ICLR, 2014
- Going Deeper with Convolutions [Paper] [Review]
- 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] [Review]
- Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson, NIPS, 2014
- Intriguing Properties of Neural Networks [Paper] [Review]
- 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] [Review]
- Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva, NIPS, 2014
- Network in Network [Paper] [Review]
- Min Lin, Qiang Chen, Shuicheng Yan, ICLR, 2014
- Neural Turing Machines [Paper] [Review]
- Alex Graves, Greg Wayne, Ivo Danihelka, ArXiv, 2014
- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation [Paper] [Review]
- Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, CVPR, 2014
- Sequence to Sequence Learning with Neural Networks [Paper] [Review]
- Ilya Sutskever, Oriol Vinyals, Quoc V. Le, NIPS, 2014
- Very Deep Convolutional Networks for Large-Scale Image Recognition [Paper] [Review]
- Karen Simonyan, Andrew Zisserman, ArXiv, 2014
- Visualizing and Understanding Convolutional Networks [Paper] [Review]
- Matthew D Zeiler, Rob Fergus, ECCV, 2014
2012