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

Sat Dec 9th 08:00 AM -- 06:30 PM @ 202
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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.
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.
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09:00 AM Lessons learned from designing Edward (Keynote) Dustin Tran
09:45 AM Tips and tricks of coding papers on PyTorch (Demonstration) Soumith Chintala
10:05 AM Differentiable Learning of Logical Rules for Knowledge Base Reasoning (Demonstration) William Cohen, Fan Yang
10:45 AM Coding Reinforcement Learning Papers (Keynote) Shangtong Zhang
11:15 AM A Linear-Time Kernel Goodness-of-Fit Test (NIPS best paper) (Demonstration) Wittawat Jitkrittum
11:35 AM Imagination-Augmented Agents for Deep Reinforcement Learning (Demonstration) Seb Racanière
11:55 AM Inductive Representation Learning on Large Graphs (Demonstration) Will Hamilton
12:15 PM Probabilistic Programming with PYRO (Demonstration) Noah Goodman
12:35 PM Poster Session (Lunch Break)
02:00 PM Simple and Efficient Implementation of Neural Nets with Automatic Operation Batching (Keynote) Graham Neubig
02:45 PM Learning Texture Manifolds with the Periodic Spatial GAN by Nikolay Jetchev , Zalando (Demonstration) Roland Vollgraf
03:05 PM MLPACK, A case study: implementing ID3 decision trees to be as fast as possible (Keynote) Ryan Curtin
03:40 PM Self-Normalizing Neural Networks (Demonstration) Tom Unterthiner
04:00 PM Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Mode (Demonstration) Jiasen Lu
04:25 PM Break <span> <a href="#"></a> </span>
05:00 PM Spotlights (Demonstration)

Author Information

Alex Dimakis (University of Texas, Austin)
Nikolaos Vasiloglou (RelationalAI)
Guy Van den Broeck (UCLA)

I am an Assistant Professor and Samueli Fellow at UCLA, in the Computer Science Department, where I direct the Statistical and Relational Artificial Intelligence (StarAI) lab. My research interests are in Machine Learning (Statistical Relational Learning, Tractable Learning), Knowledge Representation and Reasoning (Graphical Models, Lifted Probabilistic Inference, Knowledge Compilation), Applications of Probabilistic Reasoning and Learning (Probabilistic Programming, Probabilistic Databases), and Artificial Intelligence in general.

Alexander Ihler (UC Irvine)
Assaf Araki (Intel)

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