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Workshop
Sat Dec 09 08:00 AM -- 06:30 PM (PST) @ 202
NIPS Highlights (MLTrain), Learn How to code a paper with state of the art frameworks
Alexandros Dimakis · Nikolaos Vasiloglou · Guy Van den Broeck · Alexander Ihler · Assaf Araki





Workshop Home Page

Every year hundreds of papers are published at NIPS. Although the authors provide sound and scientific description and proof of their ideas, there is no space for explaining all the tricks and details that can make the implementation of the paper work. The goal of this workshop is to help authors evangelize their paper to the industry and expose the participants to all the Machine Learning/Artificial Intelligence know-how that cannot be found in the papers. Also the effect/importance of tuning parameters is rarely discussed, due to lack of space.
Submissions
We encourage you to prepare a poster of your favorite paper that explains graphically and at a higher level the concepts and the ideas discussed in it. You should also submit a jupyter notebook that explains in detail how equations in the paper translate to code. You are welcome to use any of the famous platforms like tensorFlow, Keras, MxNet, CNTK, etc.
For more information visit here
For more information https://www.mltrain.cc/

Lessons learned from designing Edward (Keynote)
Dustin Tran
Tips and tricks of coding papers on PyTorch (Demonstration)
Soumith Chintala
Differentiable Learning of Logical Rules for Knowledge Base Reasoning (Demonstration)
William Cohen, Fan Yang
Coding Reinforcement Learning Papers (Keynote)
Shangtong Zhang
A Linear-Time Kernel Goodness-of-Fit Test (NIPS best paper) (Demonstration)
Wittawat Jitkrittum
Imagination-Augmented Agents for Deep Reinforcement Learning (Demonstration)
Seb Racanière
Inductive Representation Learning on Large Graphs (Demonstration)
Will Hamilton
Probabilistic Programming with PYRO (Demonstration)
Noah Goodman
Poster Session (Lunch Break)
Simple and Efficient Implementation of Neural Nets with Automatic Operation Batching (Keynote)
Graham Neubig
Learning Texture Manifolds with the Periodic Spatial GAN by Nikolay Jetchev , Zalando (Demonstration)
Roland Vollgraf
MLPACK, A case study: implementing ID3 decision trees to be as fast as possible (Keynote)
Ryan Curtin
Self-Normalizing Neural Networks (Demonstration)
Tom Unterthiner
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Mode (Demonstration)
Jiasen Lu
Break
Spotlights (Demonstration)