4 ms·
If of interest, other notes are available at cs522.stanford.edu and more will be available shortly!
by casawa 9y ago
If of interest, other notes are available at cs522.stanford.edu and more will be available shortly!
- mchowdhury 9y ago+1
- visopsys 9y ago+1
- vrm 9y agodude nice job getting exposure with this!
- indescions_2018 9y agoYeah, definitely interested ;) Any chance the course has room for the other side of the coin? Namely, how neuroevolution and genetic strategies inform deep reinforcement learning? Am also interested in learning about the state-of-the-art in cloud based packages. I noticed recently Google released a tool called DeepVariant for use on their genomics platform. https://github.com/google/deepvariant https://github.com/google/deepvariant Creating a universal SNP and small indel variant caller with deep neural networks https://www.biorxiv.org/content/early/2018/01/09/092890 https://www.biorxiv.org/content/early/2018/01/09/092890
- chillee 9y agoAnswer: it doesn't. Genetic algorithms and the like are pretty much all terrible. They're ways of approximating your gradient, and fall to the curse of dimensionality. The only reason Uber and open.ai published their papers on evolutionary strategies (something pretty different from what people think of as genetic algorithms) is that current policy gradient methods are really bad as well, allowing what is effectively random search to do well. It's kinda like how Bayesian hyperparameter optimization is pretty terrible and 2x random search almost always beats it easily.