Aritra Dutta

21 papers receiving 170 citations

Peers

Aritra Dutta
Comparison fields: 5 of 31
  • Computational Mathematics 4
  • Computer Vision and Pattern Recognition 72
  • Artificial Intelligence 100
  • Computational Mechanics 41
  • Signal Processing 19
Replace Xinyi Xu with:
Xinyi Xu China
Hanie Sedghi United States
Roi Livni Israel
Linnan Wang United States
Chenqian Yan United States
Yaohui Cai United States
Ibrahim Alabdulmohsin Saudi Arabia
Yury Nahshan Israel
Yoshitaka Morikawa Japan
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Citations per year

Countries citing papers authored by Aritra Dutta

Since Specialization
Citations

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

Fields of papers citing papers by Aritra Dutta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Aritra Dutta, 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 Aritra Dutta Line = papers co-authored together Aritra Dutta links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202140
2 202038
3
Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation
202022
4
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning
20219
5 20199
6 20209
7 20138
8 20177
9 20177
10 20204
11 20173
12 20233
13
Weighted Low-Rank Approximation of Matrices:Some Analytical and Numerical Aspects
20163
14 20213
15 20182
16 20242
17 20241
18 20201
19 20151
20 20171

About Aritra Dutta

Aritra Dutta is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing and Information Systems, having authored 21 papers that have together received 174 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (11 papers), Stochastic Gradient Optimization Techniques (5 papers), Blind Source Separation Techniques (3 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Infrared Target Detection Methodologies (2 papers), Remote-Sensing Image Classification (2 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Computational Mathematics (4 citations), Computer Vision and Pattern Recognition (72 citations), Artificial Intelligence (100 citations), Computational Mechanics (41 citations) and Signal Processing (19 citations). Aritra Dutta has collaborated with scholars based in Saudi Arabia, United States and India. Frequent co-authors include Panos Kalnis, Ahmed M. Abdelmoniem, Marco Canini, El Houcine Bergou, Chen-Yu Ho, Hang Xu, Peter Richtárik, Xin Li, Filip Hanzely and Xin Li. Their work appears in journals such as SIAM Journal on Matrix Analysis and Applications, IEEE Transactions on Aerospace and Electronic Systems, IEEE Transactions on Signal Processing, Linear Algebra and its Applications and Archives of Pharmacy Practice.

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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