The Living Thing / Notebooks :

Probabilistic deep learning

Creating neural networks which infer whole probability densities or certainties for their predictions, rather than point estimates.

In Bayesian terms this is about estimating a posterior distribution, and in frequentist terms… uh… What is a pithy frequentist phrasing?

Anyway, AFAICT this usually boils down to doing variational inference, in which case the neural netwrok is a big approximate PDGM. Apparently you can also do simulation-based inference here, somehow using gradients? Must look into that.

Yarin Gal’s PhD Thesis summarises some stuff here: Uncertainty in Deep Learning.


Blei Lab’s software tool: Edward (source) Tensorflow indeed comes with a contributed Bayesian library called BayesFlow (Which is not the same as the cytometry library of the same name) which by contrast has documentation so perfunctory that I can’t imagine it not being easier to reimplement it.

Thomas Wiecki, Bayesian Deep Learning shows how to do it with PyMC3.

Christopher Bonnett: Mixture Density Networks with Edward, Keras and TensorFlow.


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Gal, Y. (2015) Rapid Prototyping of Probabilistic Models: Emerging Challenges in Variational Inference. In Advances in Approximate Bayesian Inference workshop, NIPS.
Gal, Y. (2016) Uncertainty in Deep Learning (phdthesis). . University of Cambridge
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