It's Friday, it's Hamming's time: Call for deep learning research problems from your "problem surplus" stack

Francois Chollet the creator of Keras tweeted the following interesting proposition:

We're still collecting interesting research problems for our open science initiative: https://t.co/yIqIhTAbSS -don't hesitate to contribute!

— François Chollet (@fchollet) 22 septembre 2016

Here it is:

Call for deep learning research problems

 

Are you a researcher? Then you probably have a "problem surplus": a list of interesting and important research problems that you don't have time to work on yourself. What if you could outsource some of these problems to distributed teams of motivated students and independent researchers looking to build experience in deep learning?You just have to submit the description of your problem, some pointers as to how to get started, and provide lightweight supervision along the way (occasionally answer questions, provide feedback, suggest experiments to try...).
What you get out of this:

• Innovative solutions to research problems that matter to you.
• Full credits for the value you provide along the research process.
• New contacts among bright people outside of your usual circles.
• A fun experience.

We are looking for both deep learning research problems, and problems from other fields that could be solved using deep learning.
Note that the information you submit here may be made public (except for your contact information). We will create a website listing the problems submitted, where people will be able to self-organize into teams dedicated to specific problems. You will be in contact with the people working on your problem via a mailing list. The research process will take place in the open, with communications being publicly available and code being released on GitHub.

 Here are some problems:

• The Curiosity Super-Resolution Challenge
• Leonardo's Challenge
• Challenging Datasets
• Ben's Chicken Challenge
• Sunday Morning Insight: The Hard Questions
• Sunday Morning Insight: The Challenges of Reddit's Sparse Admins/Mods Graphs and Sudden Phase Transitions
• Sunday Morning Insight: The Hardest Challenges We Should be Unwilling to Postpone
• Hamming's time series of blog posts
• Sunday Morning Insight: "More F$*%(g Data"

Enhanced NAVCAM image of Comet 67P/C-G taken on 18 September 2016, 12.1 km from the nucleus centre. The scale is 1.0 m/pixel and the image measures about 1.1 km across. Credits: ESA/Rosetta/NAVCAM – CC BY-SA IGO 3.0

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