Difference between revisions of "f17Stat946PaperSignUp"

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|Oct 31 || Michael Honke || || A Theoretically Grounded Application of Dropout in Recurrent Neural Networks|| [https://papers.nips.cc/paper/6241-a-theoretically-grounded-application-of-dropout-in-recurrent-neural-networks.pdf Paper] ||
 
|Oct 31 || Michael Honke || || A Theoretically Grounded Application of Dropout in Recurrent Neural Networks|| [https://papers.nips.cc/paper/6241-a-theoretically-grounded-application-of-dropout-in-recurrent-neural-networks.pdf Paper] ||
 
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|Oct 31 || Almas Rymov || || || Analytical Guarantees on Numerical Precision of Deep Neural Networks||
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|Oct 31 || Almas Rymov || || Analytical Guarantees on Numerical Precision of Deep Neural Networks|| http://proceedings.mlr.press/v70/sakr17a/sakr17a.pdf ||
 
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|Nov 2 || Prashanth T.K. || || When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications||[https://proceedings.mlr.press/v70/zhou17c/zhou17c.pdf Paper] ||
 
|Nov 2 || Prashanth T.K. || || When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications||[https://proceedings.mlr.press/v70/zhou17c/zhou17c.pdf Paper] ||

Revision as of 15:03, 3 October 2017

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 Amir-Hossein Karimi Neural Architecture Search With Reinforcement Learning [3]
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 Almas Rymov Analytical Guarantees on Numerical Precision of Deep Neural Networks http://proceedings.mlr.press/v70/sakr17a/sakr17a.pdf
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 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 [4]
Nov 9 Varshanth R Rao Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study Paper
Nov 14 Avinash Prasad Coupled GAN [5]
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 [6]
Nov 21 Aman Jhunjhunwala Curiosity-driven Exploration by Self-supervised Prediction Paper Summary
Nov 21 Peiying Li Deep Learning without Poor Local Minima [7] 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 [8]
Nov 30 Congcong Zhi Dance Dance Convolution
Nov 30 Jian Deng Automated Curriculum Learning for Neural Networks Paper
Nov 30