Charles Sutton
About
In The Last Decade
Charles Sutton
96 papers receiving 6.5k citations
Hit Papers
Peers
Comparison fields: 5 of 193
- Artificial Intelligence 3.5k
- Computer Vision and Pattern Recognition 1.5k
- Information Systems 1.4k
- Computer Networks and Communications 751
- Signal Processing 688
Countries citing papers authored by Charles Sutton
This map shows the geographic impact of Charles Sutton'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 Charles Sutton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles Sutton more than expected).
Fields of papers citing papers by Charles Sutton
This network shows the impact of papers produced by Charles Sutton. 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 Charles Sutton. The network helps show where Charles Sutton may publish in the future.
Co-authorship network of co-authors of Charles Sutton
This figure shows the co-authorship network connecting the top 25 collaborators of Charles Sutton. A scholar is included among the top collaborators of Charles Sutton based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Charles Sutton. Charles Sutton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | Learning Semantic Representations to Verify Hardware Designs | 6 |
| 3 | Latent Programmer: Discrete Latent Codes for Program Synthesis | 1 |
| 4 | Couplings for Multinomial Hamiltonian Monte Carlo | 1 |
| 5 | Global Relational Models of Source Code | 50 |
| 6 | Incremental Sampling Without Replacement for Sequence Models | 2 |
| 7 | Variational Russian Roulette for Deep Bayesian Nonparametrics | 5 |
| 8 | VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning | 96 |
| 9 | 14 | |
| 10 | Compressing LSTMs into CNNs. | 3 |
| 11 | Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation | 47 |
| 12 | 102 | |
| 13 | Quasi-Newton Methods for Markov Chain Monte Carlo | 18 |
| 14 | Inference and Learning in Networks of Queues | 6 |
| 15 | Statistical machine learning makes automatic control practical for internet datacenters | 142 |
| 16 | Exploiting machine learning to subvert your spam filter | 173 |
| 17 | Probabilistic inference in queueing networks | 4 |
| 18 | Learning in Markov Random Fields with Contrastive Free Energies | 21 |
| 19 | Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing | 3 |
| 20 | NIPS Workshop on Learning with Structured Outputs | 2 |
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.