David Dohan
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 10%
- Media Technology top 5%
- Control and Systems Engineering
- Co-authors
- Konstantinos BousmalisNathan SilbermanDilip KrishnanDumitru ErhanThomas FunkhouserLucy J. ColwellKevin J. MurphyDavid Belanger
- Topics
- Advanced Multi-Objective Optimization Algorithms (3 papers)Machine Learning and Algorithms (2 papers)Metaheuristic Optimization Algorithms Research (2 papers)
- Journals
- arXiv (Cornell University)International Conference on Machine LearningInternational Conference on Learning Representations
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
David Dohan
9 papers receiving 977 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Computer Vision and Pattern Recognition 700
- Artificial Intelligence 548
- Radiology, Nuclear Medicine and Imaging 132
- Media Technology 71
- Control and Systems Engineering 58
Countries citing papers authored by David Dohan
This map shows the geographic impact of David Dohan'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 David Dohan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Dohan more than expected).
Fields of papers citing papers by David Dohan
This network shows the impact of papers produced by David Dohan. 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 David Dohan. The network helps show where David Dohan may publish in the future.
Co-authorship network of co-authors of David Dohan
This figure shows the co-authorship network connecting the top 25 collaborators of David Dohan. A scholar is included among the top collaborators of David Dohan 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 David Dohan. David Dohan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Latent Programmer: Discrete Latent Codes for Program Synthesis | 1 |
| 2 | 6 | |
| 3 | Model-based reinforcement learning for biological sequence design | 19 |
| 4 | Amortized Bayesian Optimization over Discrete Spaces | 4 |
| 5 | 8 | |
| 6 | EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS | 0 |
| 7 | 1 | |
| 8 | Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networksbreakdown → | 942 |
| 9 | K-median Algorithms: Theory in Practice | 4 |
| 10 | 21 |
About David Dohan
David Dohan is a scholar working on Computer Graphics and Computer-Aided Design, Software and Computational Theory and Mathematics, having authored 10 papers that have together received 1.0k indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (3 papers), Machine Learning and Algorithms (2 papers) and Metaheuristic Optimization Algorithms Research (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (700 citations), Artificial Intelligence (548 citations) and Media Technology (71 citations). David Dohan has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Konstantinos Bousmalis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan, Thomas Funkhouser, Lucy J. Colwell, Kevin J. Murphy, David Belanger, Christof Angermueller and Yulia Rubanova. Their work appears in journals such as arXiv (Cornell University), International Conference on Machine Learning and International Conference on Learning Representations.
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.