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Jonathan P. Chen

dblp:228/8331 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%
Software engineering, system software, and programming languages
1 paper
Programming languages and type systems · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning
deep probabilistic models
0.412019
Pyro: Deep Universal Probabilistic Programming · J. Mach. Learn. Res. 2019
Machine learning › Probabilistic and Bayesian machine learning
probabilistic programming
0.412019
Pyro: Deep Universal Probabilistic Programming · J. Mach. Learn. Res. 2019
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference › variational inference
stochastic variational inference
0.412019
Pyro: Deep Universal Probabilistic Programming · J. Mach. Learn. Res. 2019
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
variational inference
0.412019
Pyro: Deep Universal Probabilistic Programming · J. Mach. Learn. Res. 2019
Programming languages and type systems › probabilistic programming
probabilistic programming language
0.412019
Pyro: Deep Universal Probabilistic Programming · J. Mach. Learn. Res. 2019

Methods — techniques the papers use, named apart from their topics

stochastic variational inference · 0.8GPU acceleration · 0.8
YearPublicationVenuePosition
2019 Pyro: Deep Universal Probabilistic Programming
abstract
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.
Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Paul A. Szerlip, Paul Horsfall, Noah D. Goodman
J. Mach. Learn. Res.2