Ryan P. Adams
Impact in
- Computational Theory and Mathematics top 0.1%
- Computational Drug Discovery Methods
- Advanced Multi-Objective Optimization Algorithms
- Artificial Intelligence top 0.2%
- Gaussian Processes and Bayesian Inference
- Machine Learning and Data Classification
- Machine Learning and Algorithms
Papers in
-
- Gaussian Processes and Bayesian Inference 30
- Bayesian Methods and Mixture Models 14
- Machine Learning and Algorithms 11
- Neural Networks and Applications 7
- Co-authors
- Kevin SwerskyNando de FreitasZiyu WangBobak ShahriariIain MurrayJosé Miguel Hernández-LobatoGeorge E. DahlJasper Snoek
- Journals
- Journal of Machine Learning Research (9 papers)Computer Physics Communications (1 paper)Biological Psychiatry (1 paper)ACS Central Science (1 paper)The Annals of Applied Statistics (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Ryan P. Adams
92 papers receiving 8.8k citations
Hit Papers
Peers
Comparison fields: 5 of 213
- Computational Theory and Mathematics 2.2k
- Artificial Intelligence 2.9k
- Management Science and Operations Research 658
- Computational Mathematics 26
- Computer Vision and Pattern Recognition 822
Countries citing papers authored by Ryan P. Adams
This map shows the geographic impact of Ryan P. Adams's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ryan P. Adams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan P. Adams more than expected).
Fields of papers citing papers by Ryan P. Adams
This network shows the impact of papers produced by Ryan P. Adams. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ryan P. Adams. The network helps show where Ryan P. Adams may publish in the future.
Co-authors
The 25 scholars most cited alongside Ryan P. Adams, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2024 | 3 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 0 | |
| 5 | Randomized Automatic Differentiation | 2021 | 1 |
| 6 | Amortized Finite Element Analysis for Fast PDE-Constrained Optimization | 2020 | 2 |
| 7 | SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers | 2019 | 18 |
| 8 | A Bayesian Nonparametric View on Count-Min Sketch | 2018 | 2 |
| 9 | Compressibility and Generalization in Large-Scale Deep Learning. | 2018 | 2 |
| 10 | PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference | 2017 | 3 |
| 11 | Bayesian latent structure discovery from multi-neuron recordings | 2016 | 9 |
| 12 | Composing graphical models with neural networks for structured representations and fast inference | 2016 | 58 |
| 13 | A Gaussian process model of quasar spectral energy distributions | 2015 | 1 |
| 14 | Firefly Monte Carlo: Exact MCMC with Subsets of Data | 2014 | 6 |
| 15 | Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball | 2014 | 2 |
| 16 | Bayesian optimization with unknown constraints | 2014 | 26 |
| 17 | A Physiological Time Series Dynamics-Based Approach toPatient Monitoring and Outcome Prediction | 2014 | 2 |
| 18 | Factorized point process intensities: a spatial analysis of professional | 2014 | 4 |
| 19 | Multi-Task Bayesian Optimization | 2013 | 239 |
| 20 | The Gaussian Process Density Sampler | 2008 | 23 |
About Ryan P. Adams
Ryan P. Adams is a scholar working on Computational Mathematics, Artificial Intelligence, Statistics and Probability, Signal Processing and Computational Theory and Mathematics, having authored 94 papers that have together received 9.1k indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (30 papers), Bayesian Methods and Mixture Models (14 papers), Machine Learning and Algorithms (11 papers), Advanced Multi-Objective Optimization Algorithms (8 papers), Time Series Analysis and Forecasting (8 papers), Neural Networks and Applications (7 papers), Machine Learning in Materials Science (6 papers) and Markov Chains and Monte Carlo Methods (6 papers). The work is most often cited by research in Computational Theory and Mathematics (2.2k citations), Artificial Intelligence (2.9k citations), Management Science and Operations Research (658 citations), Computational Mathematics (26 citations) and Computer Vision and Pattern Recognition (822 citations). Ryan P. Adams has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Kevin Swersky, Nando de Freitas, Ziyu Wang, Bobak Shahriari, Iain Murray, José Miguel Hernández-Lobato, George E. Dahl, Jasper Snoek, Jennifer N. Wei and David Duvenaud. Their work appears in journals such as Journal of Machine Learning Research, Computer Physics Communications, Biological Psychiatry, ACS Central Science and The Annals of Applied Statistics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.