/Deep-Learning-Papers

This repository lists up Deep Learning papers that I've read, reviewed and implemented

Deep-Learning-Papers

  • This repository lists up Deep Learning papers that I've read, reviewed and implemented (optional).

  • Click a [github] link to see the review and code on each paper.

Deep Reinforcement Learning

[1] Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." Advances in neural information processing systems (NIPS). 2013 [pdf] [github]

[2] Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." nature 529.7587 (2016): 484-489. [pdf]

[3] Jay, Nathan, et al. "A deep reinforcement learning perspective on internet congestion control." International Conference on Machine Learning (ICML). 2019. [pdf]

Computer Vision

Image Classification

[1] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). 2016. [pdf]

[2] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). 2017. [pdf]

Object Detection

[1] He, Kaiming, et al. "Mask r-cnn." Proceedings of the IEEE international conference on computer vision. 2017. [pdf]

Semantic Segmentation

[1] Jégou, Simon, et al. "The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2017. [pdf]

Language Translation

[1] Sutskever, I., O. Vinyals, and Q. V. Le. "Sequence to sequence learning with neural networks." Advances in NIPS (2014). [pdf]

[2] Wu, Yonghui, et al. "Google's neural machine translation system: Bridging the gap between human and machine translation." arXiv preprint arXiv:1609.08144 (2016). [pdf]

[3] Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems (NIPS). 2017. [pdf]

Transfer Learning & Multi-Task Learning

[1] Tan, Chuanqi, et al. "A survey on deep transfer learning." International conference on artificial neural networks. Springer, Cham, 2018. [pdf]

[2] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. [pdf]

[3] Ruder, Sebastian. "An overview of multi-task learning in deep neural networks." arXiv preprint arXiv:1706.05098 (2017). [pdf]

[4] Yosinski, Jason, et al. "How transferable are features in deep neural networks?." Advances in neural information processing systems. 2014. [pdf]

[5] Jang, Yunhun, et al. "Learning what and where to transfer." arXiv preprint arXiv:1905.05901 (2019). [pdf]

[6] Misra, Ishan, et al. "Cross-stitch networks for multi-task learning." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. [pdf]

[7] Papernot, Nicolas, et al. "Semi-supervised knowledge transfer for deep learning from private training data." arXiv preprint arXiv:1610.05755 (2016). [pdf]

[8] Kim, Eunwoo, et al. "Deep virtual networks for memory efficient inference of multiple tasks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019. [pdf]

[9] Li, Zhizhong, and Derek Hoiem. "Learning without forgetting." IEEE transactions on pattern analysis and machine intelligence 40.12 (2017): 2935-2947. [pdf]

[10] Ahn, Chanho, Eunwoo Kim, and Songhwai Oh. "Deep Elastic Networks with Model Selection for Multi-Task Learning." Proceedings of the IEEE International Conference on Computer Vision. 2019. [pdf]

[11] Sener, Ozan, and Vladlen Koltun. "Multi-task learning as multi-objective optimization." Advances in Neural Information Processing Systems. 2018. [pdf] [github]

[12] Kaiser, Lukasz, et al. "Large Scale Multi-Domain Multi-Task Learning with MultiModel." (2018). Under review at ICLR 2018. [pdf]

[13] Subramanian, Sandeep, et al. "Learning general purpose distributed sentence representations via large scale multi-task learning." arXiv preprint arXiv:1804.00079 (2018). [pdf]

[14] Zamir, Amir R., et al. "Taskonomy: Disentangling task transfer learning." Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR). 2018. [pdf]

AutoML

Data Augmentation

[1] Lim, Sungbin, et al. "Fast autoaugment." Advances in Neural Information Processing Systems. 2019. [pdf]

Neural Architecture Search (NAS)

[1] Kim, Sungwoong, et al. "Scalable neural architecture search for 3d medical image segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019. [pdf]

[2] Ying, Chris, et al. "Nas-bench-101: Towards reproducible neural architecture search." International Conference on Machine Learning (ICML). 2019. [pdf]

[3] Baker, Bowen, et al. "Designing neural network architectures using reinforcement learning." Proceedings of ICLR 2017. [pdf]