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AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

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Quoc Le: Fun AutoML-Zero experiments: Evolutionary search discovers fundamental ML algorithms from scratch, e.g., small neural nets with backprop. Can evolution be the “Master Algorithm”? ;) Paper: https://arxiv.org/abs/2003.03384 Code: https://git.io/JvKrZ https://t.co/wZQJimrLid

19 replies, 1842 likes


hardmaru: Genetic programming learned operations reminiscent of dropout, normalized gradients, and weight averaging when trying to evolve better learning algorithms. Cool work! https://arxiv.org/abs/2003.03384

4 replies, 366 likes


Jeff Dean: Neat to see evolution discover some of the approaches that human ML experts have devised over the years!

4 replies, 255 likes


Ilya Sutskever: i like how it suggests that backprop may really be the best algorithm for training neural networks https://twitter.com/quocleix/status/1237528603564204033

7 replies, 249 likes


Christian Szegedy: Amazing progress from Quoc's group

1 replies, 71 likes


Jeff Clune: AI-generating algorithms Pillars 1 and 2 take a big step forward (meta-learning the architecture and meta-learning the learning algorithm itself). Very exciting times!

0 replies, 46 likes


Per Kristian Lehre: Impressive results by Google Brain's AutoML-Zero: Evolving machine learning algorithms from scratch using genetic programming https://arxiv.org/abs/2003.03384

0 replies, 45 likes


arxiv: AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. http://arxiv.org/abs/2003.03384 https://t.co/yaKC1kHAYa

0 replies, 22 likes


Dmytro Mishkin: Tl;dr: 2-layer NN with SGD-training discovered by an evolutionary algorithm. https://t.co/tpf7nsfxMW

0 replies, 19 likes


小猫遊りょう(たかにゃし・りょう): Can evolution be the “Master Algorithm”? https://twitter.com/quocleix/status/1237528603564204033?s=20

1 replies, 12 likes


Ben Sprecher: Wow. What a fascinating, complete system for generating learning ML code from the lowest level and evolving it over time. And fully open sourced, too! @jeffclune @kenneth0stanley FYI

0 replies, 9 likes


Peter Seibel: I mean, it made all the computer programmers, so yeah.

0 replies, 9 likes


Aran Komatsuzaki: AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. Genetic-programming-like method for finding a good ML algorithm code. While the performance is far from the sota, I'm glad this kind of fun idea has been attempted. https://arxiv.org/abs/2003.03384 https://t.co/C0EtY4i1MF

0 replies, 8 likes


ALife Papers: AutoML-Zero: Evolving Machine Learning Algorithms From Scratch "a novel framework that significantly reduces human bias through a generic search space. [...] evolutionary search can still discover two-layer neural networks trained by backpropagation." https://arxiv.org/abs/2003.03384 https://t.co/jjeATqEeyK

1 replies, 5 likes


Luigi Acerbi: Very interesting work on evolutionary ML starting from simple primitives.

0 replies, 3 likes


Simone Scardapane: *Evolving Machine Learning Algorithms From Scratch* by @quocleix @crazydonkey200 et al. A simple evolutionary procedure with basic math ops can learn non-trivial ML algorithms, including model + update rule. Nice neuroevolution-related read. Preprint: https://arxiv.org/abs/2003.03384 https://t.co/vhlHmAiqwX

0 replies, 3 likes


Pierre Gutierrez: I will soon be obsolete!

0 replies, 3 likes


Sayak Paul: Woah!

0 replies, 2 likes


NIDHAL SELMI - نضال السالمي: Genetic programming leads to solutions that have "intermediate states". Self-sufficient sub parts of the whole system. This is like going from design to evolution except with artificial neural networks.

0 replies, 2 likes


Statistics Papers: AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. http://arxiv.org/abs/2003.03384

0 replies, 2 likes


Irenes (many): Whoa, cool.

0 replies, 1 likes


Gapry: AutoML-Zero: Evolving Machine Learning Algorithms From Scratch #GapryPaperReadingList #Algorithms #MachineLearning #AutoML #AutoMLZero arXiv: https://arxiv.org/abs/2003.03384 repo: https://github.com/google-research/google-research/tree/master/automl_zero#automl-zero

0 replies, 1 likes


Brijesh Singh: Can evolution be the Master Algorithm? Evolutionary search discovers fundamental ML algorithms from scratch, e.g., small neural nets with backprop. Genetic programming learned operations reminiscent of dropout, normalized gradients, & weight averaging! https://arxiv.org/abs/2003.03384

0 replies, 1 likes


@reiver ⊼ (Charles Iliya Krempeaux): 《AutoML-Zero: Evolving Machine Learning Algorithms From Scratch》 by Esteban Real, Chen Liang, David R. So, Quoc V. Le https://arxiv.org/abs/2003.03384 (machine learning) H/T @QEDanMazur

0 replies, 1 likes


BioDecoded: AutoML-Zero: Evolving Machine Learning Algorithms From Scratch | arXiv https://arxiv.org/abs/2003.03384 #AutoML https://t.co/qTyhRTxMEa

0 replies, 1 likes


Content

Found on Mar 10 2020 at https://arxiv.org/pdf/2003.03384.pdf

PDF content of a computer science paper: AutoML-Zero: Evolving Machine Learning Algorithms From Scratch