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Embeddings are not just for use in natural language processing. Here we apply embeddings to a common task in collaborative filtering – predicting user ratings – and on our way, strive for a better understanding of what an embedding layer really does.
This post builds on our recent introduction to multi-level modeling with tfprobability, the R wrapper to TensorFlow Probability. We show how to pool not just mean values (“intercepts”), but also relationships (“slopes”), thus enabling models to learn from data in an even broader way. Again, we use an example from Richard McElreath’s “Statistical Rethinking”; the
Advanced applications like generative adversarial networks, neural style transfer, and the attention mechanism ubiquitous in natural language processing used to be not-so-simple to implement with the Keras declarative coding paradigm. Now, with the advent of TensorFlow eager execution, things have changed. This post explores using eager execution with R.
More flexible models with TensorFlow eager execution and Keras Read More »
As of today, there is no mainstream road to obtaining uncertainty estimates from neural networks. All that can be said is that, normally, approaches tend to be Bayesian in spirit, involving some way of putting a prior over model weights. This holds true as well for the method presented in this post: We show how
Adding uncertainty estimates to Keras models with tfprobability Read More »
Why do we use the activations we use, and how do they relate to the cost functions they tend to co-appear with? In this post we provide a conceptual introduction.
Winner takes all: A look at activations and cost functions Read More »
Previous posts featuring tfprobability – the R interface to TensorFlow Probability – have focused on enhancements to deep neural networks (e.g., introducing Bayesian uncertainty estimates) and fitting hierarchical models with Hamiltonian Monte Carlo. This time, we show how to fit time series using dynamic linear models (DLMs), yielding posterior predictive forecasts as well as the
Like GANs, variational autoencoders (VAEs) are often used to generate images. However, VAEs add an additional promise: namely, to model an underlying latent space. Here, we first look at a typical implementation that maximizes the evidence lower bound. Then, we compare it to one of the more recent competitors, MMD-VAE, from the Info-VAE (information maximizing
TensorFlow feature columns provide useful functionality for preprocessing categorical data and chaining transformations, like bucketization or feature crossing. From R, we use them in popular “recipes” style, creating and subsequently refining a feature specification. In this post, we show how using feature specs frees cognitive resources and lets you focus on what you really want
TensorFlow feature columns: Transforming your data recipes-style Read More »
Object detection (the act of classifying and localizing multiple objects in a scene) is one of the more difficult, but very relevant in practice deep learning tasks. We’ll build up to it in several posts. Here we start with the simpler tasks of naming and locating a single object.
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 »
