Deepika Bablani

11 total papers · 590 total citations
2 papers, 58 citations indexed

About

Deepika Bablani is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Infectious Diseases. According to data from OpenAlex, Deepika Bablani has authored 2 papers receiving a total of 58 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 0 papers in Infectious Diseases. Recurrent topics in Deepika Bablani's work include Domain Adaptation and Few-Shot Learning (2 papers), Advanced Neural Network Applications (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Deepika Bablani is often cited by papers focused on Domain Adaptation and Few-Shot Learning (2 papers), Advanced Neural Network Applications (2 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Deepika Bablani collaborates with scholars based in United States. Deepika Bablani's co-authors include Dharmendra S. Modha, Jeffrey L. McKinstry, Steven K. Esser, Rathinakumar Appuswamy, Izzet B. Yildiz and John V. Arthur and has published in prestigious journals such as International Conference on Learning Representations.

In The Last Decade

Deepika Bablani

2 papers receiving 55 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Deepika Bablani 45 34 20 5 3 2 58
Shangyu Chen 38 0.8× 38 1.1× 6 0.3× 5 1.0× 3 1.0× 5 53
Joseph Hassoun 31 0.7× 30 0.9× 14 0.7× 5 1.7× 4 55
Satish Nadathur 34 0.8× 34 1.0× 13 0.7× 4 0.8× 17 5.7× 3 69
Nikolay Nikolov 25 0.6× 58 1.7× 16 0.8× 3 1.0× 3 63
Sen Zha 29 0.6× 15 0.4× 5 0.3× 11 2.2× 2 44
Seiya Tokui 30 0.7× 33 1.0× 6 0.3× 2 0.4× 6 2.0× 2 70
Arber Zela 55 1.2× 60 1.8× 6 0.3× 2 0.4× 5 77
Grace Chu 54 1.2× 52 1.5× 10 0.5× 1 0.3× 3 71
Erik De Win 19 0.4× 41 1.2× 5 0.3× 5 1.7× 4 45
Ang Li 26 0.6× 17 0.5× 10 0.5× 2 0.7× 4 41

Countries citing papers authored by Deepika Bablani

Since Specialization
Citations

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

Fields of papers citing papers by Deepika Bablani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deepika Bablani

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

All Works

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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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