Sharan Narang

23.6k total citations
7 papers, 269 citations indexed

About

Sharan Narang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Sharan Narang has authored 7 papers receiving a total of 269 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 1 paper in Statistical and Nonlinear Physics. Recurrent topics in Sharan Narang's work include Natural Language Processing Techniques (5 papers), Topic Modeling (4 papers) and Multimodal Machine Learning Applications (3 papers). Sharan Narang is often cited by papers focused on Natural Language Processing Techniques (5 papers), Topic Modeling (4 papers) and Multimodal Machine Learning Applications (3 papers). Sharan Narang collaborates with scholars based in United States, United Kingdom and China. Sharan Narang's co-authors include Colin Raffel, Adam P. Roberts, Noah Constant, Aditya Barua, Mihir Kale, Linting Xue, Rami Al‐Rfou, Erich Elsen, Hao Wu and Boris Ginsburg and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

In The Last Decade

Sharan Narang

7 papers receiving 248 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sharan Narang United States	7	192	104	21	15	11	7	269
Hongyang Zhang China	7	178 0.9×	56 0.5×	12 0.6×	14 0.9×	17 1.5×	29	227
Ji Xin Canada	9	319 1.7×	143 1.4×	41 2.0×	24 1.6×	15 1.4×	17	393
Chris Ying United States	4	189 1.0×	145 1.4×	9 0.4×	33 2.2×	12 1.1×	6	258
Anirudh Ravula United States	4	188 1.0×	76 0.7×	33 1.6×	15 1.0×	17 1.5×	4	241
Krzysztof Maziarz United Kingdom	4	85 0.4×	49 0.5×	14 0.7×	12 0.8×	11 1.0×	7	153
Hidekazu Oiwa Japan	6	215 1.1×	57 0.5×	36 1.7×	7 0.5×	6 0.5×	13	273
Jen-tse Huang China	7	142 0.7×	39 0.4×	12 0.6×	16 1.1×	34 3.1×	17	179
Beichen Zhang China	8	178 0.9×	145 1.4×	13 0.6×	6 0.4×	9 0.8×	18	285
Sherry Moore United States	3	193 1.0×	151 1.5×	6 0.3×	22 1.5×	9 0.8×	3	262
Jaejun Lee South Korea	6	175 0.9×	86 0.8×	18 0.9×	26 1.7×	15 1.4×	22	233

Countries citing papers authored by Sharan Narang

Since Specialization

Citations

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

Fields of papers citing papers by Sharan Narang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sharan Narang

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

All Works

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7 of 7 papers shown

1.

Tay, Yi, Mostafa Dehghani, Samira Abnar, et al.. (2023). Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?. 12342–12364. 18 indexed citations

2.

Liu, Rosanne, Dan Garrette, Chitwan Saharia, et al.. (2023). Character-Aware Models Improve Visual Text Rendering. 16270–16297. 11 indexed citations

3.

Gür, İzzeddin, Ofir Nachum, Yingjie Miao, et al.. (2023). Understanding HTML with Large Language Models. 2803–2821. 15 indexed citations

4.

Xue, Linting, Aditya Barua, Noah Constant, et al.. (2022). ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models. Transactions of the Association for Computational Linguistics. 10. 291–306. 114 indexed citations

5.

Narang, Sharan, Hyung Won Chung, Yi Tay, et al.. (2021). Do Transformer Modifications Transfer Across Implementations and Applications?. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5758–5773. 37 indexed citations

6.

Micikevicius, Paulius, Sharan Narang, Gregory Diamos, et al.. (2017). Mixed Precision Training. arXiv (Cornell University). 62 indexed citations

7.

Han, Song, Jeff Pool, Sharan Narang, et al.. (2016). DSD: Dense-Sparse-Dense Training for Deep Neural Networks. arXiv (Cornell University). 12 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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