This map shows the geographic impact of Maithra Raghu'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 Maithra Raghu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maithra Raghu more than expected).
This network shows the impact of papers produced by Maithra Raghu. 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 Maithra Raghu. The network helps show where Maithra Raghu may publish in the future.
Co-authorship network of co-authors of Maithra Raghu
This figure shows the co-authorship network connecting the top 25 collaborators of Maithra Raghu.
A scholar is included among the top collaborators of Maithra Raghu 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 Maithra Raghu. Maithra Raghu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Nguyen, Thao, Maithra Raghu, & Simon Kornblith. (2021). Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth. arXiv (Cornell University).52 indexed citations
2.
Ramasesh, Vinay, Ethan Dyer, & Maithra Raghu. (2021). Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics. International Conference on Learning Representations.22 indexed citations
3.
Raghu, Maithra, Chiyuan Zhang, Jon Kleinberg, & Samy Bengio. (2019). Transfusion: Understanding Transfer Learning for Medical Imaging. Neural Information Processing Systems. 32. 3342–3352.46 indexed citations
4.
Raghu, Maithra, Chiyuan Zhang, Jon Kleinberg, & Samy Bengio. (2019). Transfusion: Understanding Transfer Learning with Applications to Medical Imaging.40 indexed citations
5.
Raghu, Maithra, Katy Blumer, Rory Sayres, et al.. (2018). Direct Uncertainty Prediction with Applications to Healthcare.. arXiv (Cornell University).3 indexed citations
Gilmer, Justin, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, & Jascha Sohl‐Dickstein. (2017). Explaining the Learning Dynamics of Direct Feedback Alignment. International Conference on Learning Representations.3 indexed citations
Swaminathan, Soumya, et al.. (2008). A profile of bacteriologically confirmed pulmonary tuberculosis in children.. PubMed. 45(9). 743–7.30 indexed citations
14.
Shah, Raju, et al.. (2008). Immunogenicity and safety of an indigenously developed DTPw hepatitis B combination vaccine in Indian infants.. PubMed. 45(10). 819–23.2 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.