Search Results for author: Dave Moore

Found 9 papers, 6 papers with code

Automatic structured variational inference

2 code implementations3 Feb 2020 Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel van Gerven

However, the performance of the variational approach depends on the choice of an appropriate variational family.

Probabilistic Programming Variational Inference

Joint Distributions for TensorFlow Probability

1 code implementation22 Jan 2020 Dan Piponi, Dave Moore, Joshua V. Dillon

A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms.

Probabilistic Programming

BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding

no code implementations NeurIPS Workshop Document_Intelligen 2019 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.

Automatic Reparameterisation of Probabilistic Programs

1 code implementation ICML 2020 Maria I. Gorinova, Dave Moore, Matthew D. Hoffman

Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow modellers to directly reason about the processes generating data.

Probabilistic Programming

Effect Handling for Composable Program Transformations in Edward2

no code implementations15 Nov 2018 Dave Moore, Maria I. Gorinova

Algebraic effects and handlers have emerged in the programming languages community as a convenient, modular abstraction for controlling computational effects.

Probabilistic Programming

Simple, Distributed, and Accelerated Probabilistic Programming

1 code implementation NeurIPS 2018 Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips.

Probabilistic Programming

TensorFlow Distributions

9 code implementations28 Nov 2017 Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.

Probabilistic Programming

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