Ambedkar Dukkipati

37 papers receiving 323 citations

Peers

Ambedkar Dukkipati
Comparison fields: 5 of 73
  • Artificial Intelligence 136
  • Statistical and Nonlinear Physics 100
  • Computer Vision and Pattern Recognition 88
  • Biomedical Engineering 46
  • Radiology, Nuclear Medicine and Imaging 42
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Citations per field
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Citations per year

Countries citing papers authored by Ambedkar Dukkipati

Since Specialization
Citations

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

Fields of papers citing papers by Ambedkar Dukkipati

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ambedkar Dukkipati

This figure shows the co-authorship network connecting the top 25 collaborators of Ambedkar Dukkipati. A scholar is included among the top collaborators of Ambedkar Dukkipati 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 Ambedkar Dukkipati. Ambedkar Dukkipati 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
#WorkIndexed citations
1 0
2 0
3 2
4 5
5 56
6
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
6
7
Attentive Recurrent Comparators
13
8 6
9
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning
15
10 3
11
To go deep or wide in learning
5
12
Macaulay-Buchberger Basis Theorem for Residue Class Rings with Torsion and Border Bases over Rings.
0
13
Learning by Stretching Deep Networks
10
14
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
31
15 0
16 2
17 7
18 3
19 7
20 3

About Ambedkar Dukkipati

Ambedkar Dukkipati is a scholar working on Computational Mathematics, Algebra and Number Theory and Statistics and Probability, having authored 43 papers that have together received 337 indexed citations. Recurring topics across this work include Statistical Mechanics and Entropy (7 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Polynomial and algebraic computation (6 papers). The work is most often cited by research in Computational Mathematics (40 citations), Statistical and Nonlinear Physics (100 citations) and Computer Vision and Pattern Recognition (88 citations). Ambedkar Dukkipati has collaborated with scholars based in India, Netherlands and Switzerland. Frequent co-authors include Debarghya Ghoshdastidar, Phaneendra K. Yalavarthy, Shalabh Bhatnagar, M. Narasimha Murty, Gaurav Pandey, Pranav Shyam, Rui M. Castro, Prabhanjan Ananth, Anup Kumar and Gaurav Pandey. Their work appears in journals such as Automatica, IEEE Transactions on Medical Imaging and The Annals of Statistics.

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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