NIPS Highlights, Learn How to code a paper with state of the art frameworks
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.
Presenting the code of an algorithm published in a paper has not been easy. With the emergence of new fast prototyping systems such as TensorFlow, CNTK, PyTorch, MXNet, etc it is now much easier to present an implementation to an audience through an ipython notebook. The target group of this workshop is mainly ML Researchers/Practitioners from the industry, who want to accelerate transition of research to industrial applications.
The focus of our workshop is on making research more accessible to industry. For that reason we ask all presenters to prepare ipython notebooks that demonstrate practical examples of use. We plan to make these publicly available.
Call for Submissions
Authors and friends* of NIPS 2017 (and from other conferences) are encouraged to submit a poster of their paper along with a detailed ipython notebook that contains an implementation of their algorithms. The poster must demonstrate the ideas and concepts discussed on the paper in a high level and avoid tedious mathematical proofs and notations. The ipython notebook must be self sufficient and it must have very detailed comments and notes along with visuals that demonstrate how equations and pseudo code from the paper translates into code.
Authors are encouraged to use small datasets (real and synthetic) to limit the runtime. If the model size is too big and training time is legthy, it is ok to have pretrained models stored on a website and have the notebook download it. The authors are free to select the python platform of their choice (TensorFlow, PyTorch, CNTK, MxNet, Keras, etc).
The submissions will be judged based on the technical clarity and ease of understanding of the poster and the code. A limited number of accepted submissions will be presented during the oral sessions. The rest of the accepted submissions will be presented during a poster session. All the material will be available online.
*We will accept submissions from individuals who are not the authors of the original paper as long as they have the endorsement of the author of the original paper. The authors must be listed in the submission.
Submit your poster along with an ipython notebook by email email@example.com
- Nikolaos Vasiloglou
- Alex Dimakis
- Guy Van Der Broek
- Kristian Kersting
- Alex Ihler
- Animashree Anandkumar
- Assaf Araki
- Daniel Gordon
October 31st 2017 23:59 EST.
November 20th 2017
December 9th 2017
Lessons learned from implementing Edwards.
by Dustin Tran ,
Lessons learned from implementing MLPACK
by Ryan Curtin ,