proditus

5 ms·

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Zoubin Ghahramani Joins Uber as Chief Scientist(newsroom.uber.com)
3 points by proditus 10y ago|0 comments
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by proditus 10y ago
great points; yes, the challenge becomes considerably more challenging with MCMC!
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by proditus 10y ago
ah ok. i agree. we're working on that. :) give it a shot at let us know!
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by proditus 10y ago
you may bug me on this. i work too closely with alp :) edward does not implement completely implement advi yet. the piece that is missing is the automated transformation of constrained latent variable spaces to the real coordinate space
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by proditus 10y ago
theses are great insights. our first approach is the simplest: stochastic variational inference. consider a likelihood that factorizes over datapoints. stochastic variational inference then computes stochastic gradients of the variational o
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by proditus 10y ago
i'm assuming you're referring to building edward? installation is a bit of a pain because tensorflow is not on pypi yet. please take a look here: http://edwardlib.org/troubleshooting edward should answer some of y
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by proditus 10y ago
great questions. 1. you touch upon the right strengths of TF; that was certainly one consideration. edward is designed to address two goals that complement stan. the first is to be a platform for inference research: as such, edward is prima
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by proditus 10y ago
stan and edward dev here. happy to answer any questions. (shakir's blog posts are amazing; i recommend them all.)
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Edward: a Python library for probabilistic modeling, inference, and criticism(edwardlib.org)
7 points by proditus 10y ago|0 comments
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by proditus 11y ago
that's a really good question. some aspects of VI vs MCMC are areas of active research. so it's tough to respond succinctly, but i'll try. the key disadvantages of VI (particularly ADVI) are: 1. mean-field variational inferen
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by proditus 11y ago
glad to see the enthusiasm! ADVI [1] is a variant of BBVI [2] where we fully leverage all of the amazing things that Stan has to offer (like automatic differentiation and automatic transformations of constrained parameters). you can use AD
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by proditus 11y ago
the Stan manual [1] is like a textbook. while it's a bit long (and a fair bit longer than a research paper), i highly encourage that you take a look. it's very informative. [1] http://mc-stan.org/documentation&#x2F
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by proditus 11y ago
do let me know how it goes if you do try ADVI. we're also working on making it more robust to initialization and step-sizes. stay tuned.
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by proditus 11y ago
hi. i'm one of the Stan devs. (i work on variational inference: ADVI). happy to answer any questions here.