Misha Denil
Impact in
-
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Sentiment Analysis and Opinion Mining
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Advanced Text Analysis Techniques
Papers in
-
- Reinforcement Learning in Robotics 4
- Machine Learning and Data Classification 3
- Gaussian Processes and Bayesian Inference 2
- Domain Adaptation and Few-Shot Learning 2
- Artificial Intelligence in Games 1
-
- Multimodal Machine Learning Applications 2
- Advanced Neural Network Applications 2
- Co-authors
- Nando de Freitas (11 shared papers)Padhraic Smyth (1 shared paper)Dimitrios Kotzias (1 shared paper)David S. Matheson (2 shared papers)Hugo Larochelle (1 shared paper)Loris Bazzani (1 shared paper)Marcin Moczulski (1 shared paper)Zichao Yang (1 shared paper)
- Journals
- Neural Computation (1 paper)National Conference on Artificial Intelligence (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (7 papers)
- Partner nations
- United KingdomUnited StatesCanada
In The Last Decade
Misha Denil
12 papers receiving 701 citations
Peers
Comparison fields: 5 of 100
- Computer Vision and Pattern Recognition 331
- Artificial Intelligence 458
- Computational Mathematics 4
- Signal Processing 31
- Information Systems 56
Countries citing papers authored by Misha Denil
This map shows the geographic impact of Misha Denil'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 Misha Denil with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Misha Denil more than expected).
Fields of papers citing papers by Misha Denil
This network shows the impact of papers produced by Misha Denil. 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 Misha Denil. The network helps show where Misha Denil may publish in the future.
Co-authors
The 25 scholars most cited alongside Misha Denil, 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 | 2015 | 183 | |
| 2 | 2016 | 174 | |
| 3 | 2012 | 105 | |
| 4 | 2015 | 104 | |
| 5 | 2013 | 85 | |
| 6 | 2017 | 38 | |
| 7 | 2013 | 23 | |
| 8 | Learning to Learn for Global Optimization of Black Box Functions. | 2016 | 10 |
| 9 | Deep Apprenticeship Learning for Playing Video Games | 2015 | 6 |
| 10 | A Framework for Data-Driven Robotics | 2019 | 4 |
| 11 | Learning to Perform Physics Experiments via Deep Reinforcement Learning. | 2016 | 2 |
| 12 | The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously | 2017 | 1 |
| 13 | 2014 | 0 |
About Misha Denil
Misha Denil is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Control and Systems Engineering and Cognitive Neuroscience, having authored 13 papers that have together received 735 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Machine Learning and Data Classification (3 papers), Multimodal Machine Learning Applications (2 papers), Data Management and Algorithms (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Artificial Intelligence in Games (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (331 citations), Artificial Intelligence (458 citations), Computational Mathematics (4 citations), Signal Processing (31 citations) and Information Systems (56 citations). Misha Denil has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include Nando de Freitas, Padhraic Smyth, Dimitrios Kotzias, David S. Matheson, Hugo Larochelle, Loris Bazzani, Marcin Moczulski, Zichao Yang, Le Song and Ziyu Wang. Their work appears in journals such as Neural Computation, National Conference on Artificial Intelligence, International Conference on Learning Representations 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.