244 citations · 355 across the 7 of their papers we have counts for
7 papers
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
Gianluigi Silvestri, Emily Fertig, Dave Moore +1
Normalizing flows have shown great success as general-purpose density estimators. However, many real world applications require the use of domain-specific knowledge, which normaliz…
BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding
Emad Elwany, Dave Moore, Gaurav Oberoi
Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text. In this paper, we show that f…
Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore +5
We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single…
TensorFlow Distributions
Joshua V. Dillon, Ian Langmore, Dustin Tran +7
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 tw…
Meta-Learning MCMC Proposals
Tongzhou Wang, Yi Wu, David A. Moore +1
Effective implementations of sampling-based probabilistic inference often require manually constructed, model-specific proposals. Inspired by recent progresses in meta-learning for…
Signal-based Bayesian Seismic Monitoring
David A. Moore, Stuart J. Russell
Detecting weak seismic events from noisy sensors is a difficult perceptual task. We formulate this task as Bayesian inference and propose a generative model of seismic events and s…