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Deep Probabilistic Programming Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei https://arxiv.org/abs/1701.03757 ht

by fritzo 9y ago

Deep Probabilistic Programming Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei https://arxiv.org/abs/1701.03757 https://arxiv.org/abs/1701.03757

ineedasername 9y ago
That's very recent, and authored by d.tran, package maintainer for this project on github. Before that paper I see no other reference to it. Makes me think itwas coined in the paper and this package as part for trendy "deep" buzzword value. An impression bolstered by pyro's "about" section, which gives a vague generality and not much else. This isn't a criticism of the work done which, on looking at the details, is useful and interesting. This is a comment on the difficulty of finding out what the actual interesting bits are. I looked further: Uber's more detailed writeup has a statement for "why deep probablistic?", but it too is vague, about learning generative knowledge from data. And that a claim about generative knowledge is extremely ambitious for any learning method to make without still more explanation. EDIT: I'll be honest, it was really this project's connection to Uber, and Uber's corporate culture, that prodded me to think a bit more about the project claims & terminology use. Maybe I'm being unfair.
- fritzo 9y ago
  Thanks for being honest :) > ...difficulty of finding out what the actual interesting bits are. > Uber's more detailed writeup ... is vague We indeed struggled to balance accessibility and depth that blog post. One place to go deeper is the Pyro tutorials http://pyro.ai/examples http://pyro.ai/examples For more technical framing, take a look at Noah's paper [1]. Broadly I think the "interesting bits" are the integration of newer deep neural net methods with older Bayesian methods of flexible probabilistic modeling. [1] Deep Amortized Inference for Probabilistic Programs Daniel Ritchie, Paul Horsfall, Noah D. Goodman https://arxiv.org/abs/1610.05735 https://arxiv.org/abs/1610.05735
  - ineedasername 9y ago
    The paper clarifies a lot, and makes me think you meant something like "generative model" instead of "generative intelligence" The problem with using the later is that it only rarely (maybe a couple dozen over the last few years) appears in conjunction with ML in rigorous research publications. When it does, it is used with extremely precise and well-defined parameters (see GPRS from M.Golumbic) or used in reference to the traditional "home" of generative intelligence at the cross roads of cognitive science and pedagogical theory & methods. There, it is a close cousin and necessary component of "general intelligence." That's why its use here set off alarm bells of "Alert! Marketing speak at work! Gross overstatement of capabilities have been detected!" Because it doesn't fall much short of claiming "general intelligence" as a practical result of & use for Pyro.

by fritzo 9y ago

Deep Probabilistic Programming Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei https://arxiv.org/abs/1701.03757 https://arxiv.org/abs/1701.03757

ineedasername 9y ago

That's very recent, and authored by d.tran, package maintainer for this project on github. Before that paper I see no other reference to it. Makes me think itwas coined in the paper and this package as part for trendy "deep" buzzword value. An impression bolstered by pyro's "about" section, which gives a vague generality and not much else. This isn't a criticism of the work done which, on looking at the details, is useful and interesting. This is a comment on the difficulty of finding out what the actual interesting bits are. I looked further: Uber's more detailed writeup has a statement for "why deep probablistic?", but it too is vague, about learning generative knowledge from data. And that a claim about generative knowledge is extremely ambitious for any learning method to make without still more explanation. EDIT: I'll be honest, it was really this project's connection to Uber, and Uber's corporate culture, that prodded me to think a bit more about the project claims & terminology use. Maybe I'm being unfair.

fritzo 9y ago
Thanks for being honest :) > ...difficulty of finding out what the actual interesting bits are. > Uber's more detailed writeup ... is vague We indeed struggled to balance accessibility and depth that blog post. One place to go deeper is the Pyro tutorials http://pyro.ai/examples http://pyro.ai/examples For more technical framing, take a look at Noah's paper [1]. Broadly I think the "interesting bits" are the integration of newer deep neural net methods with older Bayesian methods of flexible probabilistic modeling. [1] Deep Amortized Inference for Probabilistic Programs Daniel Ritchie, Paul Horsfall, Noah D. Goodman https://arxiv.org/abs/1610.05735 https://arxiv.org/abs/1610.05735
- ineedasername 9y ago
  The paper clarifies a lot, and makes me think you meant something like "generative model" instead of "generative intelligence" The problem with using the later is that it only rarely (maybe a couple dozen over the last few years) appears in conjunction with ML in rigorous research publications. When it does, it is used with extremely precise and well-defined parameters (see GPRS from M.Golumbic) or used in reference to the traditional "home" of generative intelligence at the cross roads of cognitive science and pedagogical theory & methods. There, it is a close cousin and necessary component of "general intelligence." That's why its use here set off alarm bells of "Alert! Marketing speak at work! Gross overstatement of capabilities have been detected!" Because it doesn't fall much short of claiming "general intelligence" as a practical result of & use for Pyro.