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# List of Papers

# Record your contributions here:

Use the following notations:

P: You have written a summary/critique on the paper.

T: You had a technical contribution on a paper (excluding the paper that you present).

E: You had an editorial contribution on a paper (excluding the paper that you present).

Your feedback on presentations

# Paper presentation

Date | Name | Paper number | Title | Link to the paper | Link to the summary |

Oct 12 (example) | Ri Wang | Sequence to sequence learning with neural networks. | Paper | Summary | |

Oct 24 | Sakif Khan | 1 | Improved Variational Inference with Inverse Autoregressive Flow | [1] | [2] |

Oct 24 | 2 | ||||

Oct 24 | 3 | ||||

Oct 26 | 4 | ||||

Oct 26 | 5 | ||||

Oct 26 | 6 | ||||

Oct 31 | Jimit Majmudar | Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition | Paper | ||

Oct 31 | Michael Honke | A Theoretically Grounded Application of Dropout in Recurrent Neural Networks | Paper | ||

Oct 31 | |||||

Nov 2 | |||||

Nov 2 | |||||

Nov 2 | Haotian Lyu | Learning Important Features Through Propagating Activation Differences | Paper | summary | |

Nov 7 | Dishant Mittal | meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting | Paper | ||

Nov 7 | Yangjie Zhou | An Alternative Softmax Operator for Reinforcement Learning | Paper | ||

Nov 7 | Rahul Iyer | Hash Embeddings for Efficient Word Representations | NIPS 2017 Paper | ||

Nov 9 | ShuoShuo Liu | Learning the Number of Neurons in Deep Networks | Paper | ||

Nov 9 | Aravind Balakrishnan | FeUdal Networks for Hierarchical Reinforcement Learning | [3] | ||

Nov 9 | Varshanth R Rao | Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study | Paper | ||

Nov 14 | Avinash Prasad | Coupled GAN | |||

Nov 14 | Nafseer Kadiyaravida | Dialog-based Language Learning | Paper | Summary | |

Nov 14 | Ruifan Yu | Imagination-Augmented Agents for Deep Reinforcement Learning | Paper | ||

Nov 16 | Hamidreza Shahidi | Teaching Machines to Describe Images via Natural Language Feedback | |||

Nov 16 | Sachin vernekar | Natural-Parameter Networks: A Class of Probabilistic Neural Networks | Paper | Summary | |

Nov 16 | Yunqing HE | LightRNN: Memory and Computation-Efficient Recurrent Neural Networks | [4] | ||

Nov 21 | Aman Jhunjhunwala | Curiosity-driven Exploration by Self-supervised Prediction | Paper | Summary | |

Nov 21 | Peiying Li | Deep Learning without Poor Local Minima | [5] | Summary | |

Nov 21 | Ashish Gaurav | Deep Exploration via Bootstrapped DQN | Paper | Summary | |

Nov 23 | Venkateshwaran Balasubramanian | Large-Scale Evolution of Image Classifiers | Paper | ||

Nov 23 | Ershad Banijamali | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | Paper | ||

Nov 23 | Dylan Spicker | Unsupervised Domain Adaptation with Residual Transfer Networks | Paper | ||

Nov 28 | Mike Rudd | 1 | Deep Transfer Learning with Joint Adaptation Networks | Paper | Summary |

Nov 28 | Shivam Kalra | Still deciding (putting my slot) | |||

Nov 28 | Ningsheng Zhao | Robust Probabilistic Modeling with Bayesian Data Reweighting | [6] | ||

Nov 30 | Congcong Zhi | Dance Dance Convolution | |||

Nov 30 | |||||

Nov 30 |