Dustin Tran
I am a senior staff research scientist at Google DeepMind. I lead evaluation for Gemini / Bard. I also drove workstreams enabling #1 on LMSYS via frontier evaluation and posttraining research. Prior to Gemini, my most notable works are in infrastructure (Mesh TensorFlow, Tensor2Tensor, TensorFlow Probability, Edward), modeling (Image Transformer, Automatic Differentiation Variational Inference), a
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
