Variational Inference: A Review for Statisticians · HackerPapers discussions

Variational Inference: A Review for Statisticians — mentioned by cgs1019

Wed, 29 Mar 2023 15:30:01 GMT

To grok the ELBO, don't start with VAEs; start with basic variational inference. https://arxiv.org/abs/1601.00670 is good, though maybe increasingly dated...Once you're happy with VI in general, https://arxiv.org/abs/1606.05908 is a nice read on the VAE.

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Variational Inference: A Review for Statisticians — mentioned by PheonixPharts

Sat, 30 Apr 2022 15:37:23 GMT

> I might take up your challenge and write a blog post about it or something if I get any success. Success or failure, I would really enjoy seeing that write up! I would be even more excited to be proven wrong. The promise of "universal function approximator" is very temping. My personal dream would be to have it so one could essentially run scipy in reverse and learn the entire library with a NN. Even in the case I gave, the idea that you could learn an arbitrary quantile function means you could also arbitrarily learn a sampler for any distribution, since all you have to do compose a…

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Variational Inference: A Review for Statisticians — mentioned by yt-sdb

Fri, 22 Oct 2021 22:11:40 GMT

Probabilistic programming uses computer science techniques to do automated statistical modeling. For example, imagine I have a coin, and I want to discover if it is biased, i.e. if it lands on heads more often than tails. In a probabilistic programming framework, I can express my model as a simple Bernoulli model, `x ~ Bernoulli(p)`, and then automatically estimate the bias parameter `p` given some data (do "inference"). You can easily do this calculation by hand or in Python, but this does not generalize to more complex real-world scenarios. For complex probabilistic models, we must rely on…

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Variational Inference: A Review for Statisticians — mentioned by imurray

Tue, 19 Mar 2019 13:58:03 GMT

I wouldn't recommend this to someone wanting to use the currently most practical tools. But I'm glad some people want to know about this stuff. Sometimes the natural gradient turns out to be easier to compute than the gradient, and then it has made it in to applications. Variational Inference: A Review for Statisticians (Blei et al., 2016) https://arxiv.org/abs/1601.00670 In this case, the gradient (51) has the Fisher Information in it and the natural gradient gets rid of that hard-to-compute term. This stuff has been used in a bunch of applications (section 5.1, p23). You could argue that…

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Variational Inference: A Review for Statisticians — HN submission

Mon, 06 Feb 2017 12:46:48 GMT

Variational Inference: A Review for Statisticians

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Variational Inference: A Review for Statisticians — mentioned by charlescearl

Mon, 17 Oct 2016 01:02:22 GMT

While I am not statistically sophisticated to say whether the statement is true or not, it seems that the mathematical machinery is strongly converging, as witnessed by papers like [1], where some of the statistical machinery is being developed on the ML side. It might be historically interesting to note that 20-30 years ago, ML also more evenly spanned work that would be considered AI based -- as including logic or knowledge representation see [2 - 4] as examples. [1] Variational Inference: A Review for Statisticians, https://arxiv.org/abs/1601.00670 [2] A Summary of Machine Learning Papers…

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