Razvan Pascanu
Impact in
- Artificial Intelligence top 0.1%
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Anomaly Detection Techniques and Applications
- Machine Learning and ELM
- Neural Networks and Applications
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- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Human Pose and Action Recognition
Papers in
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- Domain Adaptation and Few-Shot Learning 13
- Neural Networks and Applications 11
- Reinforcement Learning in Robotics 7
- Neural Networks and Reservoir Computing 5
- Stochastic Gradient Optimization Techniques 4
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- Multimodal Machine Learning Applications 7
- Advanced Neural Network Applications 6
- Co-authors
- Yoshua Bengio (16 shared papers)Guillaume Desjardins (7 shared papers)Raia Hadsell (9 shared papers)Andrei A. Rusu (5 shared papers)Dharshan Kumaran (2 shared papers)Demis Hassabis (2 shared papers)James Kirkpatrick (2 shared papers)Agnieszka Grabska‐Barwińska (1 shared paper)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Trends in Cognitive Sciences (1 paper)Neural Networks (1 paper)Nature (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Razvan Pascanu
47 papers receiving 7.0k citations
Razvan Pascanu's Hit Papers
Peers
Comparison fields: 5 of 172
- Artificial Intelligence 4.8k
- Computer Vision and Pattern Recognition 2.7k
- Signal Processing 740
- Health Informatics 56
- Computer Networks and Communications 521
Countries citing papers authored by Razvan Pascanu
This map shows the geographic impact of Razvan Pascanu'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 Razvan Pascanu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Razvan Pascanu more than expected).
Fields of papers citing papers by Razvan Pascanu
This network shows the impact of papers produced by Razvan Pascanu. 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 Razvan Pascanu. The network helps show where Razvan Pascanu may publish in the future.
Co-authors
The 25 scholars most cited alongside Razvan Pascanu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Overcoming catastrophic forgetting in neural networks Hit paper breakdown → | 2017 | 3594 |
| 2 | Theano: A CPU and GPU Math Compiler in Python Hit paper breakdown → | 2010 | 686 |
| 3 | How to Construct Deep Recurrent Neural Networks Hit paper breakdown → | 2014 | 400 |
| 4 | 2013 | 272 | |
| 5 | 2020 | 250 | |
| 6 | 2015 | 243 | |
| 7 | Identifying and attacking the saddle point problem in high-dimensional non-convex optimization | 2014 | 231 |
| 8 | Understanding the exploding gradient problem | 2012 | 216 |
| 9 | 2016 | 212 | |
| 10 | Learning algorithms for the classification restricted Boltzmann machine | 2012 | 198 |
| 11 | 2016 | 173 | |
| 12 | Theano: Deep Learning on GPUs with Python | 2012 | 125 |
| 13 | Visual Interaction Networks: Learning a Physics Simulator from Video | 2017 | 74 |
| 14 | 2010 | 52 | |
| 15 | Deep Learners Benefit More from Out-of-Distribution Examples | 2011 | 52 |
| 16 | Imagination-Augmented Agents for Deep Reinforcement Learning | 2017 | 49 |
| 17 | Deep reinforcement learning with relational inductive biases | 2018 | 47 |
| 18 | Revisiting Natural Gradient for Deep Networks | 2014 | 43 |
| 19 | Natural Neural Networks | 2015 | 42 |
| 20 | 2016 | 42 |
About Razvan Pascanu
Razvan Pascanu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Signal Processing and Statistical and Nonlinear Physics, having authored 50 papers that have together received 7.3k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (13 papers), Neural Networks and Applications (11 papers), Multimodal Machine Learning Applications (7 papers), Reinforcement Learning in Robotics (7 papers), Advanced Neural Network Applications (6 papers), Neural Networks and Reservoir Computing (5 papers), Stochastic Gradient Optimization Techniques (4 papers) and Music and Audio Processing (4 papers). The work is most often cited by research in Artificial Intelligence (4.8k citations), Computer Vision and Pattern Recognition (2.7k citations), Signal Processing (740 citations), Health Informatics (56 citations) and Computer Networks and Communications (521 citations). Razvan Pascanu has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Yoshua Bengio, Guillaume Desjardins, Raia Hadsell, Andrei A. Rusu, Dharshan Kumaran, Demis Hassabis, James Kirkpatrick, Agnieszka Grabska‐Barwińska, Claudia Clopath and Joel Veness. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Trends in Cognitive Sciences, Neural Networks, Nature and Proceedings of the National Academy of Sciences.
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.