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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 26 Sakif Khan 1 Improved Variational Inference with Inverse Autoregressive Flow Paper Summary
Oct 26 Amir-Hossein Karimi 2 Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Paper Summary
Oct 26 Josh Valchar 3 Learning What and Where to Draw [1] Summary
Oct 31 Jimit Majmudar 4 Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition Paper Summary
Oct 31 Michael Honke 5 Universal Style Transfer via Feature Transforms Paper Summary
Oct 31 6
Nov 2 Prashanth T.K. 7 When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications Paper Summary
Nov 2
Nov 2 Haotian Lyu 9 Learning Important Features Through Propagating Activation Differences Paper summary
Nov 7 Dishant Mittal 10 meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting Paper
Nov 7 Omid Rezai 11 Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Paper
Nov 7 Rahul Iyer 12 Hash Embeddings for Efficient Word Representations NIPS 2017 Paper
Nov 9 ShuoShuo Liu 13 Learning the Number of Neurons in Deep Networks Paper Summary
Nov 9 Aravind Balakrishnan 14 FeUdal Networks for Hierarchical Reinforcement Learning [2]
Nov 9 Varshanth R Rao 15 Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study Paper Summary
Nov 14 Avinash Prasad 16 Coupled GAN [3]
Nov 14 Nafseer Kadiyaravida 17 Dialog-based Language Learning Paper Summary
Nov 14 Ruifan Yu 18 Imagination-Augmented Agents for Deep Reinforcement Learning Paper
Nov 16 Hamidreza Shahidi 19 Teaching Machines to Describe Images via Natural Language Feedback
Nov 16 Sachin vernekar 20 Natural-Parameter Networks: A Class of Probabilistic Neural Networks Paper Summary
Nov 16 Yunqing He 21 LightRNN: Memory and Computation-Efficient Recurrent Neural Networks [4] Summary
Nov 21 Aman Jhunjhunwala 22 Curiosity-driven Exploration by Self-supervised Prediction Paper Summary
Nov 21 Ashish Gaurav 24 Deep Exploration via Bootstrapped DQN Paper Summary
Nov 23 Venkateshwaran Balasubramanian 25 Large-Scale Evolution of Image Classifiers Paper
Nov 23 Ershad Banijamali 26 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Paper
Nov 23 Dylan Spicker 27 Unsupervised Domain Adaptation with Residual Transfer Networks Paper
Nov 28 Mike Rudd 28 Deep Transfer Learning with Joint Adaptation Networks Paper Summary
Nov 28 Shivam Kalra 29 Still deciding (putting my slot)
Nov 28 Aditya Sriram 30 Conditional Image Generation with PixelCNN Decoders Paper
Nov 30 Congcong Zhi 31 Dance Dance Convolution
Nov 30 Jian Deng 32 Automated Curriculum Learning for Neural Networks Paper
Nov 30 Elaheh Jalalpour 33