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Part of the r-tensorflow ecosystem, tfprobability is an R wrapper to TensorFlow Probability, the Python probabilistic programming framework developed by Google. We take the occasion of tfprobability’s acceptance on CRAN to give a high-level introduction, highlighting interesting use cases and applications.
tfprobability 0.8 on CRAN: Now how can you use it? Read More »
In a Bayesian neural network, layer weights are distributions, not tensors. Using tfprobability, the R wrapper to TensorFlow Probability, we can build regular Keras models that have probabilistic layers, and thus get uncertainty estimates “for free”. In this post, we show how to define, train and obtain predictions from a probabilistic convolutional neural network.
Variational convnets with tfprobability Read More »
Continuing our tour of applications of TensorFlow Probability (TFP), after Bayesian Neural Networks, Hamiltonian Monte Carlo and State Space Models, here we show an example of Gaussian Process Regression. In fact, what we see is a rather “normal” Keras network, defined and trained in pretty much the usual way, with TFP’s Variational Gaussian Process layer
Gaussian Process Regression with tfprobability Read More »
Kullback-Leibler divergence is not just used to train variational autoencoders or Bayesian networks (and not just a hard-to-pronounce thing). It is a fundamental concept in information theory, put to use in a vast range of applications. Most interestingly, it’s not always about constraint, regularization or compression. Quite on the contrary, sometimes it is about novelty,
Infinite surprise – the iridescent personality of Kullback-Leibler divergence Read More »
PixelCNN is a deep learning architecture – or bundle of architectures – designed to generate highly realistic-looking images. To use it, no reverse-engineering of arXiv papers or search for reference implementations is required: TensorFlow Probability and its R wrapper, tfprobability, now include a PixelCNN distribution that can be used to train a straightforwardly-defined neural network
Easy PixelCNN with tfprobability Read More »
In deep learning, there is no obvious way of obtaining uncertainty estimates. In 2016, Gal and Ghahramani proposed a method that is both theoretically grounded and practical: use dropout at test time. In this post, we introduce a refined version of this method (Gal et al. 2017) that has the network itself learn how uncertain
You sure? A Bayesian approach to obtaining uncertainty estimates from neural networks Read More »
TensorFlow Probability offers a vast range of functionality ranging from distributions over probabilistic network layers to probabilistic inference. It works seamlessly with core TensorFlow and (TensorFlow) Keras. In this post, we provide a short introduction to the distributions layer and then, use it for sampling and calculating probabilities in a Variational Autoencoder.
Getting started with TensorFlow Probability from R Read More »
Mostly when thinking of Variational Autoencoders (VAEs), we picture the prior as an isotropic Gaussian. But this is by no means a necessity. The Vector Quantised Variational Autoencoder (VQ-VAE) described in van den Oord et al’s “Neural Discrete Representation Learning” features a discrete latent space that allows to learn impressively concise latent representations. In this
Discrete Representation Learning with VQ-VAE and TensorFlow Probability Read More »
Normalizing flows are one of the lesser known, yet fascinating and successful architectures in unsupervised deep learning. In this post we provide a basic introduction to flows using tfprobability, an R wrapper to TensorFlow Probability. Upcoming posts will build on this, using more complex flows on more complex data.
Getting into the flow: Bijectors in TensorFlow Probability Read More »
Continuing from the recent introduction to bijectors in TensorFlow Probability (TFP), this post brings autoregressivity to the table. Using TFP through the new R package tfprobability, we look at the implementation of masked autoregressive flows (MAF) and put them to use on two different datasets.
Experimenting with autoregressive flows in TensorFlow Probability Read More »
