Sumit Sanghai

404 citations
3 papers · 163 · 1 hit paper · h-index 3

Impact in

    • Artificial Intelligence in Healthcare and Education
    • Topic Modeling
    • Natural Language Processing Techniques
    • Algorithms and Data Compression
    • Sentiment Analysis and Opinion Mining

Papers in

    • Topic Modeling 3
    • Natural Language Processing Techniques 3
    • Domain Adaptation and Few-Shot Learning 1
    • Speech Recognition and Synthesis 1
    • Machine Learning in Bioinformatics 1

Sumit Sanghai

3 papers receiving 156 citations

Sumit Sanghai's Hit Papers

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints 2023 · 140 citations
1400+1+2Years since publication4080120

Peers

Sumit Sanghai
Comparison fields: 5 of 55
  • Health Informatics 11
  • Artificial Intelligence 87
  • Hardware and Architecture 12
  • Computational Mathematics 1
  • Computer Vision and Pattern Recognition 20
Replace Yury Zemlyanskiy with:
Yury Zemlyanskiy United States
Hang Qi China
Jayson Lynch United States
Shilpa Choudhary India
Gaojie Jin United Kingdom
G. Sivakumar India
Trieu H. Trinh United States
Yutaro Yamada United States
Chaofan Tao Hong Kong
Maura Pintor Italy
Sumit Sanghai relative to Yury Zemlyanskiy United States Yury Zemlyanskiy's profile →
Citations per field
00.5×1.5×
Yury Zemlyanskiy · 1×
Citations per year

Countries citing papers authored by Sumit Sanghai

Since Specialization
Citations

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

Fields of papers citing papers by Sumit Sanghai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Sumit Sanghai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sumit Sanghai Line = papers co-authored together Sumit Sanghai links everyone, so they are left out of the graph.

All Works

About Sumit Sanghai

Sumit Sanghai is a scholar working on Artificial Intelligence, Molecular Biology, Infectious Diseases, Organic Chemistry and Surgery, having authored 3 papers that have together received 163 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers), Domain Adaptation and Few-Shot Learning (1 paper), Machine Learning in Bioinformatics (1 paper) and Speech Recognition and Synthesis (1 paper). The work is most often cited by research in Health Informatics (11 citations), Artificial Intelligence (87 citations), Hardware and Architecture (12 citations), Computational Mathematics (1 citation) and Computer Vision and Pattern Recognition (20 citations). Sumit Sanghai has collaborated with scholars based in United States. Frequent co-authors include Michiel de Jong, Yury Zemlyanskiy, Joshua Ainslie, James Lee-Thorp, Yun-Hsuan Sung, Santiago Ontañón, Siddhartha Brahma, David Uthus, Mandy Guo and Yi Tay.

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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