Suraj Srinivas

778 total citations
10 papers, 166 citations indexed

About

Suraj Srinivas is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Suraj Srinivas has authored 10 papers receiving a total of 166 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Statistical and Nonlinear Physics. Recurrent topics in Suraj Srinivas's work include Advanced Neural Network Applications (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Suraj Srinivas is often cited by papers focused on Advanced Neural Network Applications (4 papers), Adversarial Robustness in Machine Learning (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Suraj Srinivas collaborates with scholars based in India, Switzerland and United Kingdom. Suraj Srinivas's co-authors include R. Venkatesh Babu, François Fleuret, Markus Nagel, Tijmen Blankevoort, Lokesh Boominathan, Flávio P. Calmon, Himabindu Lakkaraju, Aniruddha Adiga and Chandra Sekhar Seelamantula and has published in prestigious journals such as Infoscience (Ecole Polytechnique Fédérale de Lausanne), arXiv (Cornell University) and ePrints@IISc (Indian Institute of Science).

In The Last Decade

Suraj Srinivas

10 papers receiving 160 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Suraj Srinivas India 4 113 91 12 10 9 10 166
Dmitry Molchanov Russia 4 134 1.2× 120 1.3× 7 0.6× 10 1.0× 12 1.3× 11 196
X. Y. Han United States 4 114 1.0× 66 0.7× 10 0.8× 13 1.3× 7 0.8× 10 180
Samaneh Azadi United States 7 60 0.5× 133 1.5× 8 0.7× 12 1.2× 14 1.6× 11 204
Danlu Chen United States 2 100 0.9× 138 1.5× 17 1.4× 9 0.9× 7 0.8× 3 188
Vikas Verma Finland 9 181 1.6× 119 1.3× 9 0.8× 11 1.1× 12 1.3× 13 241
Niv Giladi Israel 2 76 0.7× 73 0.8× 18 1.5× 12 1.2× 6 0.7× 3 162
Brandon Tran United States 7 158 1.4× 69 0.8× 11 0.9× 13 1.3× 39 4.3× 10 209
Gintare Karolina Dziugaite United Kingdom 5 88 0.8× 74 0.8× 6 0.5× 3 0.3× 9 1.0× 12 165
Tong Zhou China 6 38 0.3× 37 0.4× 13 1.1× 6 0.6× 5 0.6× 46 128
Mary Phuong Austria 4 113 1.0× 78 0.9× 12 1.0× 8 0.8× 9 1.0× 6 159

Countries citing papers authored by Suraj Srinivas

Since Specialization
Citations

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

Fields of papers citing papers by Suraj Srinivas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suraj Srinivas

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

All Works

10 of 10 papers shown
1.
Calmon, Flávio P., et al.. (2024). Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). 84298–84328. 2 indexed citations
2.
Srinivas, Suraj, et al.. (2022). Cyclical Pruning for Sparse Neural Networks. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2761–2770. 13 indexed citations
3.
Srinivas, Suraj. (2021). Gradient-based Methods for Deep Model Interpretability. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1 indexed citations
4.
Srinivas, Suraj & François Fleuret. (2019). Full-Jacobian Representation of Neural Networks. arXiv (Cornell University). 1 indexed citations
5.
Srinivas, Suraj & François Fleuret. (2019). Full-Gradient Representation for Neural Network Visualization. arXiv (Cornell University). 32. 4124–4133. 39 indexed citations
6.
Srinivas, Suraj & François Fleuret. (2018). Knowledge Transfer with Jacobian Matching. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4723–4731. 12 indexed citations
7.
Srinivas, Suraj, et al.. (2018). Estimating Confidence for Deep Neural Networks through Density modeling. 397–401. 3 indexed citations
8.
Srinivas, Suraj, et al.. (2017). Training Sparse Neural Networks. ePrints@IISc (Indian Institute of Science). 455–462. 91 indexed citations
9.
Boominathan, Lokesh, Suraj Srinivas, & R. Venkatesh Babu. (2016). Compensating for large in-plane rotations in natural images. 1–8. 3 indexed citations
10.
Srinivas, Suraj, Aniruddha Adiga, & Chandra Sekhar Seelamantula. (2014). Controlled blurring for improving image reconstruction quality in flutter-shutter acquisition. 5826–5830. 1 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.

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