Yuhuai Wu
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Computational Theory and Mathematics top 10%
- Cognitive Neuroscience
- Co-authors
- Trieu H. TrinhQuoc V. LeThang LuongHe HeYoshua BengioSaizheng ZhangThomas MesnardAsja Fischer
- Topics
- Topic Modeling (5 papers)Reinforcement Learning in Robotics (5 papers)Natural Language Processing Techniques (3 papers)
- Partner nations
- CanadaUnited StatesUnited Kingdom
In The Last Decade
Yuhuai Wu
18 papers receiving 282 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 157
- Computer Vision and Pattern Recognition 50
- Electrical and Electronic Engineering 42
- Computational Theory and Mathematics 38
- Cognitive Neuroscience 35
Countries citing papers authored by Yuhuai Wu
This map shows the geographic impact of Yuhuai Wu'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 Yuhuai Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuhuai Wu more than expected).
Fields of papers citing papers by Yuhuai Wu
This network shows the impact of papers produced by Yuhuai Wu. 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 Yuhuai Wu. The network helps show where Yuhuai Wu may publish in the future.
Co-authorship network of co-authors of Yuhuai Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Yuhuai Wu. A scholar is included among the top collaborators of Yuhuai Wu 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 Yuhuai Wu. Yuhuai Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 20 | |
| 2 | Solving olympiad geometry without human demonstrationsbreakdown → | 95 |
| 3 | 2 | |
| 4 | 20 | |
| 5 | INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving | 0 |
| 6 | Efficient Statistical Tests: A Neural Tangent Kernel Approach | 3 |
| 7 | OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning | 4 |
| 8 | 1 | |
| 9 | Modelling High-Level Mathematical Reasoning in Mechanised Declarative Proofs | 3 |
| 10 | The Importance of Sampling inMeta-Reinforcement Learning | 3 |
| 11 | Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. | 2 |
| 12 | Second-order Optimization for Deep Reinforcement Learning using Kronecker-factored Approximation | 3 |
| 13 | Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation | 27 |
| 14 | 23 | |
| 15 | 49 | |
| 16 | Architectural Complexity Measures of Recurrent Neural Networks | 16 |
| 17 | 11 | |
| 18 | 11 | |
| 19 | 3 |
About Yuhuai Wu
Yuhuai Wu is a scholar working on Artificial Intelligence, Statistics and Probability and Computer Vision and Pattern Recognition, having authored 19 papers that have together received 296 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Reinforcement Learning in Robotics (5 papers) and Natural Language Processing Techniques (3 papers). The work is most often cited by research in Health Informatics (12 citations), Computational Mathematics (3 citations) and Artificial Intelligence (157 citations). Yuhuai Wu has collaborated with scholars based in Canada, United States and United Kingdom. Frequent co-authors include Trieu H. Trinh, Quoc V. Le, Thang Luong, He He, Yoshua Bengio, Saizheng Zhang, Thomas Mesnard, Asja Fischer, Roger Grosse and David Duvenaud. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Neural Computation.
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.