Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Countries citing papers authored by Ruslan Salakhutdinov
Since
Specialization
Citations
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-authorship network of co-authors of Ruslan Salakhutdinov
This figure shows the co-authorship network connecting the top 25 collaborators of Ruslan Salakhutdinov.
A scholar is included among the top collaborators of Ruslan Salakhutdinov 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 Ruslan Salakhutdinov. Ruslan Salakhutdinov is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhao, Han, et al.. (2019). Learning Neural Networks with Adaptive Regularization. Neural Information Processing Systems. 32. 11389–11400.1 indexed citations
3.
Yang, Zhilin, Peng Qi, Saizheng Zhang, et al.. (2018). HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. 2369–2380.732 indexed citations breakdown →
4.
Chaplot, Devendra Singh, Emilio Parisotto, & Ruslan Salakhutdinov. (2018). Active Neural Localization. International Conference on Learning Representations.10 indexed citations
5.
Chen, Jia, Shizhe Chen, Qin Jin, et al.. (2018). Informedia @ TRECVID 2018: Ad-hoc Video Search, Video to Text Description, Activities in Extended video.. TRECVID.2 indexed citations
6.
Li, Chunliang, et al.. (2018). Point Cloud GAN. arXiv (Cornell University).7 indexed citations
7.
Hu, Zhiting, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, & Eric P. Xing. (2017). Controllable Text Generation.. arXiv (Cornell University).43 indexed citations
8.
Burda, Yuri, Roger Grosse, & Ruslan Salakhutdinov. (2015). {Accurate and conservative estimates of MRF log-likelihood using reverse annealing}. International Conference on Artificial Intelligence and Statistics. 102–110.12 indexed citations
9.
Kiros, Ryan, Ruslan Salakhutdinov, & Rich Zemel. (2014). Multimodal Neural Language Models. International Conference on Machine Learning. 595–603.299 indexed citations breakdown →
10.
Lake, Brenden M., Ruslan Salakhutdinov, & Josh Tenenbaum. (2013). One-shot learning by inverting a compositional causal process. DSpace@MIT (Massachusetts Institute of Technology). 26. 2526–2534.114 indexed citations
11.
Srivastava, Nitish, Ruslan Salakhutdinov, & Geoffrey E. Hinton. (2013). Modeling documents with a Deep Boltzmann Machine. Uncertainty in Artificial Intelligence. 616–624.54 indexed citations
12.
Hinton, Geoffrey E. & Ruslan Salakhutdinov. (2012). A Better Way to Pretrain Deep Boltzmann Machines. Neural Information Processing Systems. 25. 2447–2455.65 indexed citations
13.
Salakhutdinov, Ruslan, Josh Tenenbaum, & Antonio Torralba. (2011). One-Shot Learning with a Hierarchical Nonparametric Bayesian Model. DSpace@MIT (Massachusetts Institute of Technology). 195–206.47 indexed citations
14.
Salakhutdinov, Ruslan & Hugo Larochelle. (2010). Efficient Learning of Deep Boltzmann Machines. International Conference on Artificial Intelligence and Statistics. 693–700.200 indexed citations
15.
Srebro, Nathan & Ruslan Salakhutdinov. (2010). Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm. Neural Information Processing Systems. 23. 2056–2064.108 indexed citations
16.
Salakhutdinov, Ruslan. (2010). Learning Deep Boltzmann Machines using Adaptive MCMC. International Conference on Machine Learning. 943–950.39 indexed citations
17.
Lee, Jason D., Ben Recht, Nathan Srebro, Joel A. Tropp, & Ruslan Salakhutdinov. (2010). Practical Large-Scale Optimization for Max-norm Regularization. CaltechAUTHORS (California Institute of Technology). 23. 1297–1305.79 indexed citations
18.
Salakhutdinov, Ruslan & Geoffrey E. Hinton. (2009). Deep Boltzmann machines. International Conference on Artificial Intelligence and Statistics. 5. 448–455.1060 indexed citations breakdown →
19.
Hinton, Geoffrey E. & Ruslan Salakhutdinov. (2007). Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes. Neural Information Processing Systems. 20. 1249–1256.117 indexed citations
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.