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NLP News - Cat ML Papers, Multi-agent RL tool, TFGAN, MUSE, Intro to GPs, Word Mover's Distance tutorial, Gradient Boosting from scratch, Neuroevolution, More from NIPS '17

Highlights of this newsletter: A collection of ML papers on cats; a tool for multi-agent reinforcemen
NLP News
NLP News - Cat ML Papers, Multi-agent RL tool, TFGAN, MUSE, Intro to GPs, Word Mover's Distance tutorial, Gradient Boosting from scratch, Neuroevolution, More from NIPS '17
By Sebastian Ruder • Issue #13 • View online
Highlights of this newsletter: A collection of ML papers on cats; a tool for multi-agent reinforcement learning; a lightweight library for training GANs; a tool for creating unsupervised multilingual embeddings; an introduction to Gaussian Processes; a tutorial on using the Word Mover’s Distance; an introduction to Gradient Boosting; everything you need to know about Neuroevolution; many more highlights, slides, and presentations from NIPS 2017.

“[T]he algorithm is the thing we had a relationship with since the beginning. […] We learned to fuel it and do whatever it took to please the algorithm.”
- a dystopian quote by a Youtube creator on his relationship with Youtube (source: Buzzfeed)
Fun and games
The 1st Conference on Pokémonastics
Cat Paper Collection
Tools and implementations
MAgent (AAAI 2018)
TFGAN, a lightweight GAN library
Multilingual Unsupervised or Supervised Word Embeddings (MUSE)
Tutorials
Introduction to Gaussian Processes - Part I
Finding similar documents with Word2Vec and Word Mover's Distance
Gradient Boosting from scratch
Math as code: a cheat sheet for mathematical notation in code form
Deep Neuroevolution
Welcoming the Era of Deep Neuroevolution
Gradient descent vs. neuroevolution
More blog posts and articles
Putting the Linguistics in Computational Linguistics
Deep Learning Achievements Over the Past Year
In Russia, There’s an AI Helper That Makes Fun of You—and It’s Wildly Popular
Bias is not just in our datasets, it's in our conferences and community
Is AlphaZero really a scientific breakthrough in AI?
1000 different people, the same words
Everything is a Model
More from NIPS 2017
Deep Learning: Practice and Trends (NIPS 2017 Tutorial, parts I & II)
Deep Learning: Practice and Trends (NIPS 2017 Tutorial, parts I & II)
The Trouble with Bias - NIPS 2017 Keynote by Kate Crawford
The Trouble with Bias - NIPS 2017 Keynote by Kate Crawford
An Addendum to Alchemy
MLTrain Workshop materials
My talk at the inaugural Black in AI workshop dinner
Nine things I wish I had known the first time I came to NIPS
Paper picks
Objects that Sound: Unsupervised Learning of objects in images that produce sounds
Objects that Sound: Unsupervised Learning of objects in images that produce sounds
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Sebastian Ruder

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