Sainbayar Sukhbaatar
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Signal Processing
- Aerospace Engineering
- Co-authors
- Rob FergusArthur SzlamJason WestonPiotr BojanowskiÉdouard GraveArmand JoulinGabriel SynnaeveIlya Kostrikov
- Topics
- Topic Modeling (5 papers)Natural Language Processing Techniques (5 papers)Multimodal Machine Learning Applications (3 papers)
- Journals
- Repository for Publications and Research Data (ETH Zurich)arXiv (Cornell University)International Conference on Machine Learning
- Partner nations
- United StatesIsraelFrance
In The Last Decade
Sainbayar Sukhbaatar
13 papers receiving 599 citations
Hit Papers
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 511
- Computer Vision and Pattern Recognition 276
- Information Systems 52
- Signal Processing 35
- Aerospace Engineering 23
Countries citing papers authored by Sainbayar Sukhbaatar
This map shows the geographic impact of Sainbayar Sukhbaatar'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 Sainbayar Sukhbaatar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sainbayar Sukhbaatar more than expected).
Fields of papers citing papers by Sainbayar Sukhbaatar
This network shows the impact of papers produced by Sainbayar Sukhbaatar. 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 Sainbayar Sukhbaatar. The network helps show where Sainbayar Sukhbaatar may publish in the future.
Co-authorship network of co-authors of Sainbayar Sukhbaatar
This figure shows the co-authorship network connecting the top 25 collaborators of Sainbayar Sukhbaatar. A scholar is included among the top collaborators of Sainbayar Sukhbaatar 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 Sainbayar Sukhbaatar. Sainbayar Sukhbaatar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 6 | |
| 4 | 5 | |
| 5 | 31 | |
| 6 | 6 | |
| 7 | 126 | |
| 8 | 4 | |
| 9 | Intrinsic motivation and automatic curricula via asymmetric self-play | 27 |
| 10 | Composable Planning with Attributes | 4 |
| 11 | Weakly Supervised Memory Networks. | 42 |
| 12 | End-To-End Memory Networksbreakdown → | 370 |
| 13 | Learning from Noisy Labels with Deep Neural Networks | 4 |
| 14 | Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences | 1 |
| 15 | Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief Nets | 8 |
About Sainbayar Sukhbaatar
Sainbayar Sukhbaatar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 15 papers that have together received 634 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (5 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Artificial Intelligence (511 citations), Computer Vision and Pattern Recognition (276 citations) and Signal Processing (35 citations). Sainbayar Sukhbaatar has collaborated with scholars based in United States, Israel and France. Frequent co-authors include Rob Fergus, Arthur Szlam, Jason Weston, Piotr Bojanowski, Édouard Grave, Armand Joulin, Gabriel Synnaeve, Ilya Kostrikov, Zeming Lin and Oleksandr Maksymets. Their work appears in journals such as Repository for Publications and Research Data (ETH Zurich), arXiv (Cornell University) and International Conference on Machine Learning.
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.