Sam Gross

32.3k total citations · 3 hit papers
6 papers, 7.9k citations indexed

About

Sam Gross is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Sam Gross has authored 6 papers receiving a total of 7.9k indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Sam Gross's work include Topic Modeling (2 papers), Model Reduction and Neural Networks (1 paper) and Genomics and Rare Diseases (1 paper). Sam Gross is often cited by papers focused on Topic Modeling (2 papers), Model Reduction and Neural Networks (1 paper) and Genomics and Rare Diseases (1 paper). Sam Gross collaborates with scholars based in Israel and United States. Sam Gross's co-authors include Adam Lerer, Zeming Lin, Adam Paszke, Edward Z. Yang, Luca Antiga, Soumith Chintala, Alban Desmaison, Zachary DeVito, Nathan Ng and Sergey Edunov and has published in prestigious journals such as Nature Biotechnology and arXiv (Cornell University).

In The Last Decade

Sam Gross

6 papers receiving 7.5k citations

Hit Papers

align trajectories

What are hit papers?

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.

Automatic differentiation in PyTorch

2017 5.7k citations Adam Paszke, Sam Gross et al. profile →
fairseq: A Fast, Extensible Toolkit for Sequence Modeling

2019 1.4k citations Myle Ott, Sergey Edunov et al. profile →
A universal SNP and small-indel variant caller using deep neural networks

2018 757 citations Ryan Poplin, Pi-Chuan Chang et al. Nature Biotechnology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sam Gross Israel	5	3.8k	3.4k	763	579	424	6	7.9k
Jeff Clune United States	26	3.3k 0.9×	2.2k 0.7×	428 0.6×	417 0.7×	327 0.8×	58	7.2k
Xiaofeng Zhu China	54	4.4k 1.2×	4.7k 1.4×	710 0.9×	448 0.8×	1.1k 2.7×	239	10.1k
Masanori Koyama Japan	10	2.5k 0.7×	1.9k 0.6×	376 0.5×	348 0.6×	370 0.9×	32	6.3k
Arthur Gretton Germany	35	3.9k 1.0×	2.1k 0.6×	498 0.7×	678 1.2×	254 0.6×	116	7.5k
Zeming Lin China	8	2.6k 0.7×	2.9k 0.9×	3.1k 4.1×	421 0.7×	646 1.5×	19	9.2k
Kai‐Wei Chang Taiwan	18	3.4k 0.9×	2.4k 0.7×	981 1.3×	549 0.9×	162 0.4×	55	7.1k
Ulrike von Luxburg Germany	25	3.5k 0.9×	2.6k 0.8×	839 1.1×	723 1.2×	225 0.5×	60	7.9k
Shuiwang Ji United States	43	3.5k 0.9×	3.1k 0.9×	996 1.3×	451 0.8×	784 1.8×	134	7.4k
Fuhui Long United States	20	3.0k 0.8×	2.4k 0.7×	2.4k 3.1×	652 1.1×	651 1.5×	31	9.5k
Neil D. Lawrence United Kingdom	49	3.8k 1.0×	1.8k 0.5×	1.2k 1.6×	663 1.1×	112 0.3×	172	8.4k

Countries citing papers authored by Sam Gross

Since Specialization

Citations

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

Fields of papers citing papers by Sam Gross

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sam Gross

This figure shows the co-authorship network connecting the top 25 collaborators of Sam Gross. A scholar is included among the top collaborators of Sam Gross 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 Sam Gross. Sam Gross is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Bakhtin, Anton, Yuntian Deng, Sam Gross, et al.. (2020). Energy-Based Models for Text. arXiv (Cornell University). 2 indexed citations

2.

Ott, Myle, Sergey Edunov, Alexei Baevski, et al.. (2019). fairseq: A Fast, Extensible Toolkit for Sequence Modeling. 48–53. 1381 indexed citations breakdown →

3.

Poplin, Ryan, Pi-Chuan Chang, David H. Alexander, et al.. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology. 36(10). 983–987. 757 indexed citations breakdown →

4.

Gross, Sam, Marc’Aurelio Ranzato, & Arthur Szlam. (2017). Hard Mixtures of Experts for Large Scale Weakly Supervised Vision. 5085–5093. 33 indexed citations

5.

Paszke, Adam, Sam Gross, Soumith Chintala, et al.. (2017). Automatic differentiation in PyTorch. 5707 indexed citations breakdown →

6.

Lerer, Adam, Sam Gross, & Rob Fergus. (2016). Learning Physical Intuition of Block Towers by Example. arXiv (Cornell University). 430–438. 23 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.

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