ChanServ changed the topic of #mlpack to: "mlpack: a fast, flexible machine learning library :: We don't always respond instantly, but we will respond; please be patient :: Logs at http://www.mlpack.org/irc/
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< jenkins-mlpack2> Project docker mlpack weekly build build #90: STILL UNSTABLE in 6 hr 48 min: http://ci.mlpack.org/job/docker%20mlpack%20weekly%20build/90/
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< jenkins-mlpack2> Project docker mlpack nightly build build #551: UNSTABLE in 3 hr 2 min: http://ci.mlpack.org/job/docker%20mlpack%20nightly%20build/551/
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< travis-ci> mlpack/ensmallen#519 (2.10.5 - 892b4c4 : Ryan Curtin): The build passed.
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< rcurtin> I think that when we release ensmallen 2.11.0, I want to call it "The Poster Session Is Full" :)
< zoq> :) nice idea
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< rcurtin> should we merge https://github.com/mlpack/mlpack/pull/2072 ?
< zoq> It's ready from my side.
< rcurtin> awesome, I will merge then since it has two approvals
< rcurtin> but I didn't want to jump into a PR I was not involved with :)
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< metahost> Are genetic algorithms planned for ensmallen?
< metahost> I have seen CNE and DE optimizers available for non-differentiable functions
< zoq> metahost: Absolutely, already looked into NSGA-II but haven't had the time yet to work on an implementation.
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< rcurtin> seems like that would be a great fit for the categorical function types (at least the "traditional" genetic algorithms)
< rcurtin> in fact maybe I should open some issues for, e.g., extending simulated annealing to categorical functions
< rcurtin> I met with an old mlpack contributor recently; he's connected to people doing the IBM LALE auto-ML project, and they had interest in wrapping mlpack and also using ensmallen for hyperparameter optimization
< rcurtin> so perhaps there could be a lot of momentum for categorical function optimization
< rcurtin> or at least that's something I've always found exciting :)
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