Ruslan Salakhutdinov
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Artificial Intelligence top 0.01%
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
Papers in
-
- Topic Modeling 42
- Natural Language Processing Techniques 29
- Domain Adaptation and Few-Shot Learning 27
-
- Multimodal Machine Learning Applications 31
- Generative Adversarial Networks and Image Synthesis 30
- Advanced Image and Video Retrieval Techniques 13
- Advanced Neural Network Applications 12
- Co-authors
- Geoffrey E. Hinton (17 shared papers)Nitish Srivastava (9 shared papers)Ilya Sutskever (3 shared papers)Alex Krizhevsky (1 shared paper)Andriy Mnih (3 shared papers)Richard S. Zemel (4 shared papers)Joshua B. Tenenbaum (7 shared papers)Gregory Koch (1 shared paper)
- Journals
- Journal of Machine Learning Research (2 papers)IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)Science (2 papers)Cognitive Science (2 papers)Scientific Data (1 paper)
- Partner nations
- United StatesCanadaIsrael
In The Last Decade
Ruslan Salakhutdinov
150 papers receiving 59.6k citations
Ruslan Salakhutdinov's Hit Papers
Peers
Comparison fields: 5 of 231
- Computer Vision and Pattern Recognition 22.4k
- Artificial Intelligence 29.2k
- Signal Processing 6.0k
- Computational Mathematics 277
- Media Technology 2.7k
Countries citing papers authored by Ruslan Salakhutdinov
This map shows the geographic impact of Ruslan Salakhutdinov'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 Ruslan Salakhutdinov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruslan Salakhutdinov more than expected).
Fields of papers citing papers by Ruslan Salakhutdinov
This network shows the impact of papers produced by Ruslan Salakhutdinov. 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 Ruslan Salakhutdinov. The network helps show where Ruslan Salakhutdinov may publish in the future.
Co-authors
The 25 scholars most cited alongside Ruslan Salakhutdinov, 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 155 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Dropout: a simple way to prevent neural networks from overfitting Hit paper breakdown → | 2014 | 24594 |
| 2 | Reducing the Dimensionality of Data with Neural Networks Hit paper breakdown → | 2006 | 15271 |
| 3 | Probabilistic Matrix Factorization Hit paper breakdown → | 2007 | 2732 |
| 4 | Siamese Neural Networks for One-shot Image Recognition Hit paper breakdown → | 2015 | 2016 |
| 5 | Human-level concept learning through probabilistic program induction Hit paper breakdown → | 2015 | 1563 |
| 6 | Restricted Boltzmann machines for collaborative filtering Hit paper breakdown → | 2007 | 1345 |
| 7 | Neighbourhood Components Analysis Hit paper breakdown → | 2004 | 1279 |
| 8 | Deep Boltzmann machines Hit paper breakdown → | 2009 | 1152 |
| 9 | Bayesian probabilistic matrix factorization using Markov chain Monte Carlo Hit paper breakdown → | 2008 | 1070 |
| 10 | Semantic hashing Hit paper breakdown → | 2008 | 963 |
| 11 | HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering Hit paper breakdown → | 2018 | 833 |
| 12 | Evaluation methods for topic models Hit paper breakdown → | 2009 | 618 |
| 13 | Multimodal Learning with Deep Boltzmann Machines Hit paper breakdown → | 2012 | 467 |
| 14 | Deep learning for neuroimaging: a validation study Hit paper breakdown → | 2014 | 439 |
| 15 | Skip-Thought Vectors Hit paper breakdown → | 2015 | 422 |
| 16 | An Efficient Learning Procedure for Deep Boltzmann Machines Hit paper breakdown → | 2012 | 361 |
| 17 | Hamming Distance Metric Learning Hit paper breakdown → | 2012 | 358 |
| 18 | Multimodal Neural Language Models Hit paper breakdown → | 2014 | 337 |
| 19 | One shot learning of simple visual concepts | 2011 | 312 |
| 20 | Replicated Softmax: an Undirected Topic Model Hit paper breakdown → | 2009 | 306 |
About Ruslan Salakhutdinov
Ruslan Salakhutdinov is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computational Mechanics and Statistical and Nonlinear Physics, having authored 155 papers that have together received 62.5k indexed citations. Recurring topics across this work include Topic Modeling (42 papers), Multimodal Machine Learning Applications (31 papers), Generative Adversarial Networks and Image Synthesis (30 papers), Natural Language Processing Techniques (29 papers), Domain Adaptation and Few-Shot Learning (27 papers), Music and Audio Processing (16 papers), Advanced Image and Video Retrieval Techniques (13 papers) and Advanced Neural Network Applications (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (22.4k citations), Artificial Intelligence (29.2k citations), Signal Processing (6.0k citations), Computational Mathematics (277 citations) and Media Technology (2.7k citations). Ruslan Salakhutdinov has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Geoffrey E. Hinton, Nitish Srivastava, Ilya Sutskever, Alex Krizhevsky, Andriy Mnih, Richard S. Zemel, Joshua B. Tenenbaum, Gregory Koch, Brenden M. Lake and Sam T. Roweis. Their work appears in journals such as Journal of Machine Learning Research, IEEE Transactions on Pattern Analysis and Machine Intelligence, Science, Cognitive Science and Scientific Data.
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.