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You are here		blog.shakirm.com A Year of Approximate Inference: Review of the NIPS 2015 Workshop - The Spectator
\|	\|	dustintran.com Discussion of "Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing" \| Dusti...	10.1 parsecs away Travel
\|	\|	This article is written with much help by David Blei. It is extracted from a discussion paper on "Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing". [link] We commend Wand (2016) for an excellent description of message passing (mp) and for developing it to infer large semiparametric regression models. We agree with the author in fully embracing the modular nature of message passing, where one can define "fragments" that enable us to compose localized a...	10.1 parsecs away Travel
\|	\|	tiao.io A Tutorial on Variational Autoencoders with a Concise Keras Implementation \| Louis Tiao	10.1 parsecs away Travel
\|	\|	An in-depth practical guide to variational encoders from a probabilistic perspective.	10.1 parsecs away Travel
\|	\|	resources.paperdigest.org Paper Digest: NIPS 2018 Highlights - Resources \| Paper Digest	13.3 parsecs away Travel
\|	\|	Download NIPS-2018-Paper-Digests.pdf- highlights of all NIPS-2018 papers. The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. In 2018, it is to be held in Montreal, Canada. There were ~4,800 paper submissions, of which 1009	13.3 parsecs away Travel
\|	\|	nonint.com The it in AI models is the dataset. Non_Interactive Software & ML	80.2 parsecs away Travel
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