Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of Manish Kumar'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 Manish Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manish Kumar more than expected).
This network shows the impact of papers produced by Manish Kumar. 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 Manish Kumar. The network helps show where Manish Kumar may publish in the future.
Co-authorship network of co-authors of Manish Kumar
This figure shows the co-authorship network connecting the top 25 collaborators of Manish Kumar.
A scholar is included among the top collaborators of Manish Kumar 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 Manish Kumar. Manish Kumar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Romera‐Paredes, Bernardino, Mohammadamin Barekatain, Alexander Novikov, et al.. (2023). Mathematical discoveries from program search with large language models. Nature. 625(7995). 468–475.124 indexed citations breakdown →
Raut, Ashwin, et al.. (2021). Improving Classification Accuracy Using Data Fusion Technique in IoT.. Journal of the Association for Information Systems. 142.1 indexed citations
12.
Kumar, Manish, et al.. (2020). Training Neural Networks for and by Interpolation. Oxford University Research Archive (ORA) (University of Oxford). 1. 799–809.5 indexed citations
Kumar, Manish, et al.. (2020). Neural Network Branching for Neural Network Verification. International Conference on Learning Representations.1 indexed citations
Zisserman, Andrew, et al.. (2016). Trusting SVM for Piecewise Linear CNNs. Oxford University Research Archive (ORA) (University of Oxford).1 indexed citations
Hanumanthappa, M., et al.. (2014). A Study of Information Extraction Tools forOnline English Newspapers (PDF):Comparative Analysis. International Journal of Innovative Research in Computer and Communication Engineering. 2(10). 6112–6119.1 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.