Discover and read the best of Twitter Threads about #DLindaba2018

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David Silver on Principles for Reinforcement Learning at the #DLIndaba2018. Important principles that are not only applicable to RL, but to ML research in general. E.g. leaderboard-driven research vs. hypothesis-driven research (see the slides below).
Principle 2. How an algorithm scales is more important than its starting point. Avoid performance ceilings. Deep Learning is successful because it scales so effectively.
Principles are meant to be controversial. I would argue that sample efficiency is at least as important.
Principle 3. Generality (how your algorithm performs on other tasks) is super important. Key is to design a diverse set of challenging tasks.
This. We should evaluate on out of distribution data and new tasks.
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Dave Silver tells us his principles of Deep RL. 1. Evaluation drives progress. #DLIndaba2018
Principles of RL number 2: Scalability determines success.
principles of RL number 3: generality future-proofs our algorithms. #DLIndaba2018
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@DeepIndaba top tips from an extremely useful panel discussion on how to write a great research paper:
- tell one story throughout the paper. make sure this story is clear and that you are able to frame your paper in 1 sentence.
- be clear about what the problem is and about the solution you are putting forward.
- you don't have to put everything you did in everything you write.
- be academically honest. don't oversell your results.
- introduce a concept and its mathematical notation at the same time to keep your reader in the loop.
- spend time on creating images that help to visualize concepts, put them at the top of the page with a self-contained caption.
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