deep learning

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Surveys

1) Survey on representation learning: http://arxiv.org/pdf/1206.5538.pdf

2) Building Machines That Learn and Think Like People: http://arxiv.org/abs/1604.00289

Transfer Leraning

3) Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning: http://arxiv.org/pdf/1511.06342v4.pdf

4) Progressive Neural Networks: https://arxiv.org/pdf/1606.04671v3.pdf

5) Distilling the knowledge in a Neural Network: https://www.cs.toronto.edu/~hinton/absps/distillation.pdf

6) Policy distillation: http://arxiv.org/pdf/1511.06295v2.pdf

(Inverse) Reinforcement Leraning

7) Deep Inverse Reinforcement Learning: http://www.cs.utexas.edu/~sniekum/classes/RLFD-F15/papers/Wulfmeier15.pdf

8) An actor-critic algorithm for sequence prediction: https://arxiv.org/pdf/1607.07086v2.pdf

9) professor forcing: a new algorithm for training recurrent networks

Encoder-decoder && adversarial learning

10) Generative Adversarial Nets: http://datascienceassn.org/sites/default/files/Generative%20Adversarial%20Nets.pdf

11) InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets: http://arxiv.org/abs/1606.03657

12) Auto-Encoding Variational Bayes: http://arxiv.org/abs/1312.6114

13) Adversarially learned Inference: https://arxiv.org/pdf/1606.00704v1.pdf

14) Unsupervised representation learning with deep convolutional generative adversarial networks

15) Adversarial feature learning

Attention and related

16) Show, Attend and Tell: Neural Image Caption Generation with Visual Attention: http://jmlr.org/proceedings/papers/v37/xuc15.pdf

17) DRAW: A Recurrent Neural Network For Image Generation: https://arxiv.org/pdf/1502.04623v2.pdf

18) Attend, Infer, Repeat: Fast Scene Understanding with Generative Models: http://arxiv.org/abs/1603.08575

19) Recurrent models of visual attention

(Visual) question answer

20) Learning to Compose Neural Networks for Question Answering: http://arxiv.org/pdf/1601.01705.pdf

New articeture of CNN

21) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks: https://arxiv.org/pdf/1506.01497.pdf

22) Residual Network: https://arxiv.org/pdf/1512.03385v1.pdf

23) Identity mapping: https://arxiv.org/pdf/1603.05027.pdf

24) Highway network: https://arxiv.org/pdf/1505.00387v2.pdf

25) Deep Networks with stochastic depth: https://arxiv.org/pdf/1603.09382v3.pdf

RNN/LSTM/Vedio

26) Pixel Recurrent Neural Networks: https://arxiv.org/pdf/1601.06759v3.pdf

27) Structual-RNN: Deep learning on spatio-Temporal graphs

28) Leraning to forget: continual prediction with LSTM

29) Training Recurrent Neural Networks by diffusion

30) Unsupervised learning of video representations using LSTMs

31) Describing videos by exploiting temporal structure

32) Delving deeper into convolutional networks for learning video representations

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