Rajat Raina

5.6k total citations · 2 hit papers
11 papers, 2.0k citations indexed

About

Rajat Raina is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Rajat Raina has authored 11 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Signal Processing. Recurrent topics in Rajat Raina's work include Machine Learning and Algorithms (4 papers), Machine Learning and Data Classification (3 papers) and Speech and Audio Processing (2 papers). Rajat Raina is often cited by papers focused on Machine Learning and Algorithms (4 papers), Machine Learning and Data Classification (3 papers) and Speech and Audio Processing (2 papers). Rajat Raina collaborates with scholars based in United States and France. Rajat Raina's co-authors include Andrew Y. Ng, Honglak Lee, Alexis Battle, Anand Madhavan, Daphne Koller, Andrew McCallum, Roger Grosse, Christopher D. Manning, Alex Teichman and Chi Wang and has published in prestigious journals such as Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)), International Joint Conference on Artificial Intelligence and National Conference on Artificial Intelligence.

In The Last Decade

Rajat Raina

10 papers receiving 1.9k citations

Hit Papers

Self-taught learning 2007 2026 2013 2019 2007 2009 250 500 750 1000

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rajat Raina United States 10 1.2k 823 234 172 148 11 2.0k
SingerYoram 6 1.2k 1.0× 644 0.8× 183 0.8× 167 1.0× 229 1.5× 8 1.9k
Dennis DeCoste United States 16 1.0k 0.8× 924 1.1× 157 0.7× 131 0.8× 226 1.5× 39 2.1k
宏治 津田 Japan 1 1.1k 0.9× 867 1.1× 165 0.7× 115 0.7× 172 1.2× 2 2.0k
Raman Arora United States 17 1.0k 0.9× 1.0k 1.3× 381 1.6× 149 0.9× 84 0.6× 72 2.1k
Francesco Camastra Italy 19 900 0.7× 779 0.9× 223 1.0× 68 0.4× 98 0.7× 39 1.8k
Weihua Ou China 25 720 0.6× 1.3k 1.6× 233 1.0× 160 0.9× 129 0.9× 121 2.1k
Genevieve Orr United States 8 777 0.6× 550 0.7× 185 0.8× 90 0.5× 63 0.4× 26 1.7k
Pascal Lamblin Canada 7 831 0.7× 742 0.9× 282 1.2× 65 0.4× 75 0.5× 9 1.9k
Yan Yan China 19 760 0.6× 1.1k 1.4× 232 1.0× 206 1.2× 72 0.5× 88 2.0k
Bernd Fritzke Germany 11 1.6k 1.3× 1.0k 1.2× 300 1.3× 113 0.7× 79 0.5× 14 2.4k

Countries citing papers authored by Rajat Raina

Since Specialization
Citations

This map shows the geographic impact of Rajat Raina'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 Rajat Raina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rajat Raina more than expected).

Fields of papers citing papers by Rajat Raina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rajat Raina. 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 Rajat Raina. The network helps show where Rajat Raina may publish in the future.

Co-authorship network of co-authors of Rajat Raina

This figure shows the co-authorship network connecting the top 25 collaborators of Rajat Raina. A scholar is included among the top collaborators of Rajat Raina 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 Rajat Raina. Rajat Raina is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Huang, Ying, et al.. (2020). User Taste-Aware Image Search. 3301–3304.
2.
3.
Lee, Honglak, Rajat Raina, Alex Teichman, & Andrew Y. Ng. (2009). Exponential family sparse coding with applications to self-taught learning. International Joint Conference on Artificial Intelligence. 1113–1119. 47 indexed citations
4.
Raina, Rajat, Anand Madhavan, & Andrew Y. Ng. (2009). Large-scale deep unsupervised learning using graphics processors. 873–880. 421 indexed citations breakdown →
5.
Ng, Andrew Y. & Rajat Raina. (2009). Self-taught learning. 14 indexed citations
6.
Grosse, Roger, et al.. (2007). Shift-invariant sparse coding for audio classification. Uncertainty in Artificial Intelligence. 149–158. 116 indexed citations
7.
Raina, Rajat, et al.. (2007). Self-taught learning. 759–766. 1012 indexed citations breakdown →
8.
Raina, Rajat, Andrew Y. Ng, & Daphne Koller. (2006). Constructing informative priors using transfer learning. 713–720. 200 indexed citations
9.
Raina, Rajat, Andrew Y. Ng, & Christopher D. Manning. (2005). Robust textual inference via learning and abductive reasoning. National Conference on Artificial Intelligence. 1099–1105. 41 indexed citations
10.
Raina, Rajat, Aria Haghighi, Christopher Cox, et al.. (2005). Robust Textual Inference using Diverse Knowledge Sources. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 22 indexed citations
11.
Raina, Rajat, et al.. (2003). Classification with Hybrid Generative/Discriminative Models. Scholarworks (University of Massachusetts Amherst). 16. 545–552. 142 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026