Jacob Buckman
Impact in
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
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- Advanced Malware Detection Techniques
Papers in
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- Natural Language Processing Techniques 1
- Adversarial Robustness in Machine Learning 1
- Reinforcement Learning in Robotics 1
- Topic Modeling 1
- Explainable Artificial Intelligence (XAI) 1
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- Multimodal Machine Learning Applications 1
- Co-authors
- Ian Goodfellow (1 shared paper)Aurko Roy (1 shared paper)Colin Raffel (1 shared paper)George Tucker (1 shared paper)Eugene Brevdo (1 shared paper)Honglak Lee (1 shared paper)Danijar Hafner (1 shared paper)Chris Dyer (1 shared paper)
- Journals
- Repositori digital de la UPF (Universitat Pompeu Fabra) (1 paper)International Conference on Learning Representations (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United States
In The Last Decade
Jacob Buckman
3 papers receiving 193 citations
Peers
Comparison fields: 5 of 39
- Artificial Intelligence 185
- Signal Processing 42
- Computer Vision and Pattern Recognition 52
- Hardware and Architecture 15
- Health Informatics 1
Countries citing papers authored by Jacob Buckman
This map shows the geographic impact of Jacob Buckman'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 Jacob Buckman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob Buckman more than expected).
Fields of papers citing papers by Jacob Buckman
This network shows the impact of papers produced by Jacob Buckman. 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 Jacob Buckman. The network helps show where Jacob Buckman may publish in the future.
Co-authors
The 9 scholars most cited alongside Jacob Buckman, 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 | Thermometer Encoding: One Hot Way To Resist Adversarial Examples | 2018 | 194 |
| 2 | Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion | 2018 | 10 |
| 3 | 2016 | 5 |
About Jacob Buckman
Jacob Buckman is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Theory and Mathematics and Infectious Diseases, having authored 3 papers that have together received 209 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (1 paper), Natural Language Processing Techniques (1 paper), Adversarial Robustness in Machine Learning (1 paper), Reinforcement Learning in Robotics (1 paper), Model Reduction and Neural Networks (1 paper), Topic Modeling (1 paper), Explainable Artificial Intelligence (XAI) (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Artificial Intelligence (185 citations), Signal Processing (42 citations), Computer Vision and Pattern Recognition (52 citations), Hardware and Architecture (15 citations) and Health Informatics (1 citation). Jacob Buckman has collaborated with scholars based in United States. Frequent co-authors include Ian Goodfellow, Aurko Roy, Colin Raffel, George Tucker, Eugene Brevdo, Honglak Lee, Danijar Hafner, Chris Dyer and Miguel Ballesteros. Their work appears in journals such as Repositori digital de la UPF (Universitat Pompeu Fabra), International Conference on Learning Representations and Neural Information Processing Systems.
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.