Dustin Tran
I am a staff research scientist at Google DeepMind. I am broadly interested in intelligence under themes like programs and probability. Currently, I lead evaluation for Bard and also work on Gemini. My most notable works are in infrastructure (Mesh TensorFlow, Tensor2Tensor, TensorFlow Probability, Edward), modeling (Image Transformer, Automatic Differentiation Variational Inference), and evaluati
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable methods for generating samples and computing statistics, e.g., log density. Bijectors
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An introduction to probabilistic programming, now available in TensorFlow Probability
