Ryan P. Adams

Source: Scopus
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1 Finished
1 Active

RI: Small: Accelerating Machine Learning via Randomized Automatic Differentiation
Adams, R. P.
NSF - National Science Foundation
10/1/20 → 9/30/23
Project: Research project
RI: Small: Parallel Markov Chain Monte Carlo Methods for Scalable Bayesian Inference
Adams, R. P.
NSF - National Science Foundation
9/1/17 → 8/31/20
Project: Research project

7096 Citations
30 h-Index
44 Conference contribution
22 Conference article
14 Article
6 Paper
More
- 3 Chapter
- 3 Review article

A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation
Xue, T., Beatson, A., Chiaramonte, M., Roeder, G., Ash, J. T., Menguc, Y., Adriaenssens, S., Adams, R. P. & Mao, S., Aug 28 2020, In: Soft matter. 16, 32, p. 7524-7534 11 p.
Research output: Contribution to journal › Article › peer-review

Open Access
1 Scopus citations
Approximate inference for constructing astronomical catalogs from images
The Voleon Group, Sep 2019, In: Annals of Applied Statistics. 13, 3, p. 1884-1926 43 p.
Research output: Contribution to journal › Article › peer-review

Open Access
3 Scopus citations
Discrete object generation with reversible inductive construction
Seff, A., Zhou, W., Damani, F., Doyle, A. & Adams, R. P., 2019, In: Advances in Neural Information Processing Systems. 32
Research output: Contribution to journal › Conference article › peer-review
Efficient optimization of loops and limits with randomized telescoping sums
Beatson, A. & Adams, R. P., Jan 1 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 836-854 19 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
3 Scopus citations
Non-vacuous generalization bounds at the Im-AGeNet scale: A Pac-Bayesian compression approach
Zhou, W., Veitch, V., Austern, M., Adams, R. P. & Orbanz, P., Jan 1 2019.
Research output: Contribution to conference › Paper › peer-review

RI: Small: Accelerating Machine Learning via Randomized Automatic Differentiation

RI: Small: Parallel Markov Chain Monte Carlo Methods for Scalable Bayesian Inference

A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation

Approximate inference for constructing astronomical catalogs from images

Discrete object generation with reversible inductive construction

Efficient optimization of loops and limits with randomized telescoping sums

Non-vacuous generalization bounds at the Im-AGeNet scale: A Pac-Bayesian compression approach