`

( events)   Timezone: »  
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
Alex 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)
Tips and tricks of coding papers on PyTorch (Demonstration)
Differentiable Learning of Logical Rules for Knowledge Base Reasoning (Demonstration)
Coding Reinforcement Learning Papers (Keynote)
A Linear-Time Kernel Goodness-of-Fit Test (NIPS best paper) (Demonstration)
Imagination-Augmented Agents for Deep Reinforcement Learning (Demonstration)
Inductive Representation Learning on Large Graphs (Demonstration)
Probabilistic Programming with PYRO (Demonstration)
Poster Session (Lunch Break)
Simple and Efficient Implementation of Neural Nets with Automatic Operation Batching (Keynote)
Learning Texture Manifolds with the Periodic Spatial GAN by Nikolay Jetchev , Zalando (Demonstration)
MLPACK, A case study: implementing ID3 decision trees to be as fast as possible (Keynote)
Self-Normalizing Neural Networks (Demonstration)
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Mode (Demonstration)
Break
Spotlights (Demonstration)