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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		Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling	Paper	[1]
Oct 26	Josh Valchar	6	Learning What and Where to Draw	[2]
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	Almas Rymov		Analytical Guarantees on Numerical Precision of Deep Neural Networks	Paper
Nov 2	Prashanth T.K.		When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications	Paper
Nov 2	Aditya Sriram		Conditional Image Generation with PixelCNN Decoders	Paper
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		Sequential Neural Models with Stochastic Layers	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	[4]
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	[5]	Summary
Nov 21	Aman Jhunjhunwala		Curiosity-driven Exploration by Self-supervised Prediction	Paper	Summary
Nov 21	Peiying Li		Deep Learning without Poor Local Minima	[6]	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	[7]
Nov 30	Congcong Zhi		Dance Dance Convolution
Nov 30	Jian Deng		Automated Curriculum Learning for Neural Networks	Paper
Nov 30	Elaheh Jalalpour