Jacob Menick
Impact in
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Domain Adaptation and Few-Shot Learning
- Evolutionary Algorithms and Applications
- Adversarial Robustness in Machine Learning
- Topic Modeling
Papers in
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- Topic Modeling 2
- Explainable Artificial Intelligence (XAI) 2
- Domain Adaptation and Few-Shot Learning 2
- AI in cancer detection 1
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- Advanced Neural Network Applications 2
- Generative Adversarial Networks and Image Synthesis 2
- Advanced Data Compression Techniques 1
- Co-authors
- Mohammad Gheshlaghi Azar (1 shared paper)Ian Osband (1 shared paper)Olivier Pietquin (1 shared paper)Meire Fortunato (1 shared paper)Bilal Piot (1 shared paper)Alexander Graves (1 shared paper)Rémi Munos (1 shared paper)Demis Hassabis (1 shared paper)
- Journals
- International Conference on Learning Representations (2 papers)International Conference on Machine Learning (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United KingdomUnited StatesFrance
In The Last Decade
Jacob Menick
6 papers receiving 164 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 120
- Computational Mathematics 2
- Computer Vision and Pattern Recognition 61
- Automotive Engineering 14
- Computer Networks and Communications 20
Countries citing papers authored by Jacob Menick
This map shows the geographic impact of Jacob Menick'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 Menick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob Menick more than expected).
Fields of papers citing papers by Jacob Menick
This network shows the impact of papers produced by Jacob Menick. 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 Menick. The network helps show where Jacob Menick may publish in the future.
Co-authors
The 25 scholars most cited alongside Jacob Menick, 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 | Noisy Networks For Exploration | 2018 | 115 |
| 2 | Multiplicative Interactions and Where to Find Them | 2020 | 20 |
| 3 | Rigging the Lottery: Making All Tickets Winners | 2020 | 19 |
| 4 | 2018 | 14 | |
| 5 | Practical Real Time Recurrent Learning with a Sparse Approximation | 2021 | 3 |
| 6 | Associative Compression Networks | 2018 | 2 |
About Jacob Menick
Jacob Menick is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Biophysics and Infectious Diseases, having authored 6 papers that have together received 173 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Advanced Neural Network Applications (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), AI in cancer detection (1 paper), Advanced Data Compression Techniques (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Artificial Intelligence (120 citations), Computational Mathematics (2 citations), Computer Vision and Pattern Recognition (61 citations), Automotive Engineering (14 citations) and Computer Networks and Communications (20 citations). Jacob Menick has collaborated with scholars based in United Kingdom, United States and France. Frequent co-authors include Mohammad Gheshlaghi Azar, Ian Osband, Olivier Pietquin, Meire Fortunato, Bilal Piot, Alexander Graves, Rémi Munos, Demis Hassabis, Charles Blundell and Shane Legg. Their work appears in journals such as International Conference on Learning Representations, International Conference on Machine Learning and arXiv (Cornell University).
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.