Demo Data from EDBT 2026 / reviewers in the wild / expert
Jonathan P. Chen
dblp:228/8331
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning
deep probabilistic models |
0.4 | 1 | 2019 | Pyro: Deep Universal Probabilistic Programming · J. Mach. Learn. Res. 2019 |
Machine learning › Probabilistic and Bayesian machine learning
probabilistic programming |
0.4 | 1 | 2019 | 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 |
0.4 | 1 | 2019 | Pyro: Deep Universal Probabilistic Programming · J. Mach. Learn. Res. 2019 |
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
variational inference |
0.4 | 1 | 2019 | Pyro: Deep Universal Probabilistic Programming · J. Mach. Learn. Res. 2019 |
Programming languages and type systems › probabilistic programming
probabilistic programming language |
0.4 | 1 | 2019 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Pyro: Deep Universal Probabilistic ProgrammingabstractPyro 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 |