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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]
Oct 31	Jimit Majmudar	4	Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition	Paper
Oct 31	Michael Honke	5	A Theoretically Grounded Application of Dropout in Recurrent Neural Networks	Paper
Oct 31	Almas Rymov	6	Analytical Guarantees on Numerical Precision of Deep Neural Networks	Paper
Nov 2	Prashanth T.K.	7	When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications	Paper
Nov 2	Aditya Sriram	8	Conditional Image Generation with PixelCNN Decoders	Paper
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	Yangjie Zhou	11	Sequential Neural Models with Stochastic Layers	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
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
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	Peiying Li	23	Deep Learning without Poor Local Minima	[5]	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		30
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