Misha Denil

3.6k total citations
13 papers, 731 citations indexed

About

Misha Denil is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Misha Denil has authored 13 papers receiving a total of 731 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 3 papers in Signal Processing. Recurrent topics in Misha Denil's work include Reinforcement Learning in Robotics (4 papers), Machine Learning and Data Classification (3 papers) and Advanced Neural Network Applications (2 papers). Misha Denil is often cited by papers focused on Reinforcement Learning in Robotics (4 papers), Machine Learning and Data Classification (3 papers) and Advanced Neural Network Applications (2 papers). Misha Denil collaborates with scholars based in United Kingdom, United States and Canada. Misha Denil's co-authors include Nando de Freitas, Dimitrios Kotzias, Padhraic Smyth, David S. Matheson, Loris Bazzani, Hugo Larochelle, Ziyu Wang, Zichao Yang, Le Song and Marcin Moczulski and has published in prestigious journals such as Neural Computation, arXiv (Cornell University) and International Conference on Learning Representations.

In The Last Decade

Misha Denil

12 papers receiving 694 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Misha Denil United Kingdom 8 460 329 56 52 40 13 731
Kuo-Kun Tseng China 14 269 0.6× 243 0.7× 40 0.7× 35 0.7× 34 0.8× 49 737
Feihu Zhang China 11 275 0.6× 403 1.2× 72 1.3× 101 1.9× 31 0.8× 18 843
Yidong Chen China 16 501 1.1× 274 0.8× 54 1.0× 21 0.4× 29 0.7× 99 899
Dylan Anderson United States 4 267 0.6× 294 0.9× 41 0.7× 31 0.6× 22 0.6× 10 556
Wee Sun Lee Singapore 10 492 1.1× 218 0.7× 58 1.0× 80 1.5× 63 1.6× 15 745
Joshua Ainslie United States 8 497 1.1× 212 0.6× 62 1.1× 23 0.4× 29 0.7× 15 776
Yuguang Yan China 16 506 1.1× 290 0.9× 51 0.9× 17 0.3× 24 0.6× 38 718
Weina Fu China 15 347 0.8× 280 0.9× 132 2.4× 43 0.8× 37 0.9× 50 765

Countries citing papers authored by Misha Denil

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Misha Denil

This figure shows the co-authorship network connecting the top 25 collaborators of Misha Denil. A scholar is included among the top collaborators of Misha Denil 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 Misha Denil. Misha Denil is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Cabi, Serkan, Sergio Gómez Colmenarejo, Alexander Novikov, et al.. (2019). A Framework for Data-Driven Robotics. arXiv (Cornell University). 4 indexed citations
2.
Cabi, Serkan, et al.. (2017). The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously. 207–216. 1 indexed citations
3.
Maheswaranathan, Niru, Matthew W. Hoffman, Sergio Gómez Colmenarejo, et al.. (2017). Learned Optimizers that Scale and Generalize. arXiv (Cornell University). 3751–3760. 38 indexed citations
4.
Denil, Misha, Pulkit Agrawal, Tejas D. Kulkarni, et al.. (2016). Learning to Perform Physics Experiments via Deep Reinforcement Learning.. International Conference on Learning Representations. 2 indexed citations
5.
Hoffman, Matthew W., et al.. (2016). Learning to Learn for Global Optimization of Black Box Functions.. arXiv (Cornell University). 10 indexed citations
6.
Mirowski, Piotr, Razvan Pascanu, Fabio Viola, et al.. (2016). Learning to Navigate in Complex Environments. arXiv (Cornell University). 173 indexed citations
7.
Denil, Misha, et al.. (2015). Deep Apprenticeship Learning for Playing Video Games. National Conference on Artificial Intelligence. 6 indexed citations
8.
Kotzias, Dimitrios, Misha Denil, Nando de Freitas, & Padhraic Smyth. (2015). From Group to Individual Labels Using Deep Features. 597–606. 182 indexed citations
9.
Yang, Zichao, Marcin Moczulski, Misha Denil, et al.. (2015). Deep Fried Convnets. 1476–1483. 104 indexed citations
10.
Denil, Misha, et al.. (2014). Distributed Parameter Estimation in Probabilistic Graphical Models. arXiv (Cornell University). 27. 1700–1708.
11.
Denil, Misha, et al.. (2013). Consistency of Online Random Forests. arXiv (Cornell University). 1256–1264. 23 indexed citations
12.
Denil, Misha, David S. Matheson, & Nando de Freitas. (2013). Narrowing the Gap: Random Forests In Theory and In Practice. arXiv (Cornell University). 665–673. 84 indexed citations
13.
Denil, Misha, Loris Bazzani, Hugo Larochelle, & Nando de Freitas. (2012). Learning Where to Attend with Deep Architectures for Image Tracking. Neural Computation. 24(8). 2151–2184. 104 indexed citations

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.

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