Jeff Smith

26 papers receiving 737 citations

Jeff Smith's Hit Papers

PyTorch distributed 2020 · 248 citations
2480+2+4Years since publication50100150200

Peers

Jeff Smith
Comparison fields: 5 of 113
  • Computational Mathematics 5
  • Genetics 221
  • Hardware and Architecture 45
  • Endocrinology 29
  • Computer Vision and Pattern Recognition 96
Replace Thomas Anantharaman with:
Thomas Anantharaman United States
David B. Knoester United States
Sebastian Deorowicz Poland
Andrew Butterfield Ireland
Henry E. Schaffer United States
Zhiyi Zhang China
Thomas E. Gorochowski United Kingdom
Marie‐France Sagot France
Mary Beth Kery United States
J. Peterson United States
Jeff Smith relative to Thomas Anantharaman United States Thomas Anantharaman's profile →
Citations per field
00.5×11.5×
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 →
2020248
2 2001106
3 200060
4 201058
5 201451
6 201030
7 201129
8 199828
9 198427
10 201120
11 201617
12 201414
13 200713
14 201313
15 201112
16 20207
17 20135
18 20153
19 20212
20 20132

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 754 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), Bacteriophages and microbial interactions (3 papers), RNA and protein synthesis mechanisms (3 papers), Bacterial Genetics and Biotechnology (2 papers), Slime Mold and Myxomycetes Research (2 papers) and Biocrusts and Microbial Ecology (2 papers). The work is most often cited by research in Computational Mathematics (5 citations), Genetics (221 citations), Hardware and Architecture (45 citations), Endocrinology (29 citations) and Computer Vision and Pattern Recognition (96 citations). Jeff Smith has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Edward P. Nikonowicz, Joan E. Strassmann, David C. Queller, J. David Van Dyken, Teng Li, Adam Paszke, Rohan Varma, Soumith Chintala, Li Shen and Peter C. Zee. Their work appears in journals such as Evolution, Proceedings of the Royal Society B Biological Sciences, Biochemistry, Political Research Quarterly and The American Naturalist.

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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