Shirish Shevade

5.3k total citations · 3 hit papers
47 papers, 3.4k citations indexed

About

Shirish Shevade is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Shirish Shevade has authored 47 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 18 papers in Computer Vision and Pattern Recognition and 12 papers in Information Systems. Recurrent topics in Shirish Shevade's work include Face and Expression Recognition (16 papers), Topic Modeling (9 papers) and Text and Document Classification Technologies (8 papers). Shirish Shevade is often cited by papers focused on Face and Expression Recognition (16 papers), Topic Modeling (9 papers) and Text and Document Classification Technologies (8 papers). Shirish Shevade collaborates with scholars based in India, Singapore and United States. Shirish Shevade's co-authors include S. Sathiya Keerthi, Chiranjib Bhattacharyya, K. R. K. Murthy, Rahul Gupta, Aditya Kanade, M. Narasimha Murty, S. Asharaf, A.N. Poo, Aditya Kanade and Vinod Ganapathy and has published in prestigious journals such as Bioinformatics, Hepatology and Pattern Recognition.

In The Last Decade

Shirish Shevade

43 papers receiving 3.2k citations

Hit Papers

Improvements to Platt's SMO Algorithm for SVM Classifier ... 2000 2026 2008 2017 2001 2000 2017 400 800 1.2k
What are hit papers?

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.

  1. Improvements to Platt's SMO Algorithm for SVM Classifier Design
    2001 1.3k citations S. Sathiya Keerthi, Shirish Shevade et al. Neural Computation profile →
  2. Improvements to the SMO algorithm for SVM regression
    2000 656 citations Shirish Shevade, S. Sathiya Keerthi et al. IEEE Transactions on Neural Networks profile →
  3. DeepFix: Fixing Common C Language Errors by Deep Learning
    2017 233 citations Rahul Gupta, Shirish Shevade et al. profile →

Peers

Shirish Shevade
Markus Hagenbuchner Australia
Vasile Palade United Kingdom
Shengding Hu China
Chiranjib Bhattacharyya India
M. Gori Italy
Rong Chen China
Ganqu Cui China
Irina Rish United States
BengioYoshua
Zhengyan Zhang China
Markus Hagenbuchner Australia
Shirish Shevade
Citations per year, relative to Shirish Shevade Shirish Shevade (= 1×) peers Markus Hagenbuchner

Countries citing papers authored by Shirish Shevade

Since Specialization
Citations

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

Fields of papers citing papers by Shirish Shevade

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shirish Shevade

This figure shows the co-authorship network connecting the top 25 collaborators of Shirish Shevade. A scholar is included among the top collaborators of Shirish Shevade 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 Shirish Shevade. Shirish Shevade 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.
Shevade, Shirish, et al.. (2021). ReXPlug: Explainable Recommendation using Plug-and-Play Language Model. 81–91. 29 indexed citations
2.
Gupta, Rahul, Aditya Kanade, & Shirish Shevade. (2019). Neural Attribution for Semantic Bug-Localization in Student Programs. Neural Information Processing Systems. 32. 11861–11871. 13 indexed citations
3.
Shevade, Shirish, et al.. (2017). A Dominant Strategy Truthful, Deterministic Multi-Armed Bandit Mechanism with Logarithmic Regret. arXiv (Cornell University). 1667–1669.
4.
Deepak, P, Dinesh Garg, & Shirish Shevade. (2017). Latent Space Embedding for Retrieval in Question-Answer Archives. Research Portal (Queen's University Belfast). 855–865. 1 indexed citations
5.
Shevade, Shirish, et al.. (2016). Topic Model Based Multi-Label Classification. 996–1003. 3 indexed citations
6.
Ganu, Tanuja, Shirish Shevade, & S. Sundararajan. (2013). Sparse Max-Margin Multiclass and Multi-label Classifier Design for Fast Inference. 423–431. 2 indexed citations
7.
Balamurugan, P., et al.. (2013). Optimizing F-measure with non-convex loss and sparse linear classifiers. 1–8. 9 indexed citations
8.
Balamurugan, P., et al.. (2012). Sequential Alternating Proximal Method for Scalable Sparse Structural SVMs. 61–70. 5 indexed citations
9.
Sellamanickam, Sundararajan, et al.. (2012). Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses. arXiv (Cornell University). 1091–1100. 4 indexed citations
10.
Shevade, Shirish, et al.. (2010). A fast quasi-Newton method for semi-supervised SVM. Pattern Recognition. 44(10-11). 2305–2313. 31 indexed citations
11.
Sundararajan, S., et al.. (2009). Semi-supervised classification using sparse Gaussian process regression. NOT FOUND REPOSITORY (Indian Institute of Science Bangalore). 1193–1198. 3 indexed citations
12.
Shevade, Shirish & S. Sundararajan. (2009). Validation-Based Sparse Gaussian Process Classifier Design. Neural Computation. 21(7). 2082–2103.
13.
Keerthi, S. Sathiya & Shirish Shevade. (2007). A Fast Tracking Algorithm for Generalized LARS/LASSO. IEEE Transactions on Neural Networks. 18(6). 1826–1830. 20 indexed citations
14.
Sundararajan, S., Shirish Shevade, & S. Sathiya Keerthi. (2006). Fast Generalized Cross-Validation Algorithm for Sparse Model Learning. Neural Computation. 19(1). 283–301. 5 indexed citations
15.
Keerthi, S. Sathiya, et al.. (2005). A Fast Dual Algorithm for Kernel Logistic Regression. Machine Learning. 61(1-3). 151–165. 91 indexed citations
16.
Nam, Suk Woo, Jik Young Park, Adaikalavan Ramasamy, et al.. (2005). Molecular Changes From Dysplastic Nodule to Hepatocellular Carcinoma Through Gene Expression Profiling *. Hepatology. 42(4). 809–818. 133 indexed citations
17.
Shevade, Shirish & S. Sathiya Keerthi. (2003). A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics. 19(17). 2246–2253. 291 indexed citations
18.
Keerthi, S. Sathiya & Shirish Shevade. (2003). SMO Algorithm for Least-Squares SVM Formulations. Neural Computation. 15(2). 487–507. 80 indexed citations
19.
Keerthi, S. Sathiya, Shirish Shevade, Chiranjib Bhattacharyya, & K. R. K. Murthy. (2000). A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Transactions on Neural Networks. 11(1). 124–136. 255 indexed citations
20.
Shevade, Shirish, S. Sathiya Keerthi, Chiranjib Bhattacharyya, & K. R. K. Murthy. (2000). Improvements to the SMO algorithm for SVM regression. IEEE Transactions on Neural Networks. 11(5). 1188–1193. 656 indexed citations breakdown →

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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