Le Song
-
- Advanced Neural Network Applications 13
- Signal Processing top 0.5%
- Artificial Intelligence top 0.2%
- Topic Modeling 12
- Gaussian Processes and Bayesian Inference 12
- Neural Networks and Applications 11
- Advanced Graph Neural Networks 10
- Domain Adaptation and Few-Shot Learning 9
- Machine Learning and Data Classification 9
- Machine Learning and Algorithms 8
- Computational Mathematics top 5%
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)Journal of Machine Learning Research (2 papers)Journal of Geodesy (1 paper)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Le Song
94 papers receiving 5.3k citations
Hit Papers
Peers
Comparison fields: 5 of 172
- Computer Vision and Pattern Recognition 2.2k
- Signal Processing 985
- Artificial Intelligence 2.8k
- Computational Mathematics 29
- Health Information Management 177
Countries citing papers authored by Le Song
This map shows the geographic impact of Le Song'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 Le Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Le Song more than expected).
Fields of papers citing papers by Le Song
This network shows the impact of papers produced by Le Song. 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 Le Song. The network helps show where Le Song may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Le Song, 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 | 2024 | 2 | |
| 2 | IDRQA: Iterative Document Reranking for Open-domain Multi-hop Question Answering | 2021 | 1 |
| 3 | DDRQA: Dynamic Document Reranking for Open-domain Multi-hop Question Answering. | 2020 | 2 |
| 4 | Bandit Samplers for Training Graph Neural Networks. | 2020 | 1 |
| 5 | Compressive Hyperspherical Energy Minimization. | 2019 | 1 |
| 6 | Meta Particle Flow for Sequential Bayesian Inference. | 2019 | 1 |
| 7 | Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning | 2019 | 1 |
| 8 | Learning Steady-States of Iterative Algorithms over Graphs | 2018 | 59 |
| 9 | Decoupled Networks | 2018 | 7 |
| 10 | Neural Model-Based Reinforcement Learning for Recommendation | 2018 | 4 |
| 11 | Learning Loop Invariants for Program Verification | 2018 | 19 |
| 12 | Smoothed Dual Embedding Control. | 2017 | 2 |
| 13 | Iterative machine teaching | 2017 | 17 |
| 14 | On the Complexity of Learning Neural Networks | 2017 | 3 |
| 15 | The Nonparametric Kernel Bayes Smoother | 2016 | 6 |
| 16 | Diversity Leads to Generalization in Neural Networks. | 2016 | 4 |
| 17 | Nonparametric Tree Graphical Models. | 2010 | 14 |
| 18 | A Kernel Statistical Test of Independence | 2007 | 317 |
| 19 | Colored Maximum Variance Unfolding | 2007 | 56 |
| 20 | Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface | 2005 | 19 |
About Le Song
Le Song is a scholar working on Computational Mathematics, Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing and Software, having authored 95 papers that have together received 5.5k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (13 papers), Topic Modeling (12 papers), Gaussian Processes and Bayesian Inference (12 papers), Neural Networks and Applications (11 papers), Advanced Graph Neural Networks (10 papers), Domain Adaptation and Few-Shot Learning (9 papers), Machine Learning and Data Classification (9 papers) and Machine Learning and Algorithms (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.2k citations), Signal Processing (985 citations), Artificial Intelligence (2.8k citations), Computational Mathematics (29 citations) and Health Information Management (177 citations). Le Song has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Zhiding Yu, Weiyang Liu, Bhiksha Raj, Yandong Wen, Ming Li, Arthur Gretton, Alex Smola, Kenji Fukumizu, Hanjun Dai and Alexander J. Smola. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Machine Learning Research, Journal of Geodesy, IEEE Signal Processing Magazine and IEEE Transactions on Parallel and Distributed Systems.
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.