Jeff Smith

26 papers receiving 745 citations

Jeff Smith's Hit Papers

PyTorch distributed 2020 · 257 citations
2570+2+4Years since publication50100150200250

Peers

Jeff Smith
Comparison fields: 5 of 111
  • Computational Mathematics 5
  • Genetics 212
  • Hardware and Architecture 45
  • Endocrinology 29
  • Computer Vision and Pattern Recognition 98
Replace Thomas Anantharaman with:
Thomas Anantharaman United States
David B. Knoester United States
Glenn Rowe United Kingdom
Sebastian Deorowicz Poland
Andrew Butterfield Ireland
Henry E. Schaffer United States
Zhiyi Zhang China
Ran Libeskind-Hadas United States
Thomas E. Gorochowski United Kingdom
Paul Medvedev United States
Jeff Smith relative to Thomas Anantharaman United States Thomas Anantharaman's profile →
Citations per field
00.5×6.2×
Thomas Anantharaman · 1×
Citations per year

Countries citing papers authored by Jeff Smith

Since Specialization
Citations

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

Fields of papers citing papers by Jeff Smith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1
PyTorch distributed
Hit paper breakdown →
2020257
2 2001106
3 200060
4 201059
5 201451
6 201030
7 201129
8 199828
9 198426
10 201120
11 201617
12 201414
13 200713
14 201313
15 201112
16 20207
17 20135
18 20153
19 20042
20 20212

About Jeff Smith

Jeff Smith is a scholar working on Sociology and Political Science, Genetics, Molecular Biology, Ecology, Evolution, Behavior and Systematics and Political Science and International Relations, having authored 26 papers that have together received 763 indexed citations. Recurring topics across this work include Evolution and Genetic Dynamics (10 papers), Evolutionary Game Theory and Cooperation (9 papers), Plant and animal studies (4 papers), RNA and protein synthesis mechanisms (3 papers), Bacteriophages and microbial interactions (3 papers), Electoral Systems and Political Participation (2 papers), Mathematical and Theoretical Epidemiology and Ecology Models (2 papers) and Receptor Mechanisms and Signaling (2 papers). The work is most often cited by research in Computational Mathematics (5 citations), Genetics (212 citations), Hardware and Architecture (45 citations), Endocrinology (29 citations) and Computer Vision and Pattern Recognition (98 citations). Jeff Smith has collaborated with scholars based in United States, Switzerland and Poland. Frequent co-authors include Edward P. Nikonowicz, David C. Queller, Joan E. Strassmann, J. David Van Dyken, Li Shen, Teng Li, Adam Paszke, Soumith Chintala, Rohan Varma and Peter C. Zee. Their work appears in journals such as Evolution, Biochemistry, Proceedings of the Royal Society B Biological Sciences, Neuropharmacology and Diagnostic Microbiology and Infectious Disease.

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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