Benoit Steiner

41.9k total citations
5 papers, 213 citations indexed

About

Benoit Steiner is a scholar working on Computer Vision and Pattern Recognition, Structural Biology and Information Systems. According to data from OpenAlex, Benoit Steiner has authored 5 papers receiving a total of 213 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Vision and Pattern Recognition, 1 paper in Structural Biology and 1 paper in Information Systems. Recurrent topics in Benoit Steiner's work include Advanced Neural Network Applications (2 papers), Modular Robots and Swarm Intelligence (1 paper) and Manufacturing Process and Optimization (1 paper). Benoit Steiner is often cited by papers focused on Advanced Neural Network Applications (2 papers), Modular Robots and Swarm Intelligence (1 paper) and Manufacturing Process and Optimization (1 paper). Benoit Steiner collaborates with scholars based in United States and Israel. Benoit Steiner's co-authors include Jonathan Ragan‐Kelley, Riyadh Baghdadi, Luke Anderson, Frédo Durand, Steven Johnson, Tzu‐Mao Li, Kayvon Fatahalian, Andrew Adams, Michaël Gharbi and Azalia Mirhoseini and has published in prestigious journals such as ACM Transactions on Graphics and International Conference on Learning Representations.

In The Last Decade

Benoit Steiner

5 papers receiving 206 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Benoit Steiner United States	4	121	105	83	61	37	5	213
Christina Giannoula Greece	6	130 1.1×	31 0.3×	47 0.6×	109 1.8×	99 2.7×	21	235
Ehsan Totoni United States	10	220 1.8×	31 0.3×	36 0.4×	207 3.4×	52 1.4×	21	295
Sam Ainsworth United Kingdom	10	209 1.7×	57 0.5×	104 1.3×	167 2.7×	53 1.4×	24	301
Danyang Zhuo United States	9	52 0.4×	31 0.3×	53 0.6×	195 3.2×	47 1.3×	29	251
Gabriel Rodríguez Spain	10	144 1.2×	13 0.1×	41 0.5×	158 2.6×	53 1.4×	35	237
Adrián Castelló Spain	9	123 1.0×	75 0.7×	61 0.7×	113 1.9×	54 1.5×	45	224
Viktor Prasanna United States	5	64 0.5×	30 0.3×	89 1.1×	71 1.2×	32 0.9×	29	172
Apan Qasem United States	8	164 1.4×	21 0.2×	40 0.5×	135 2.2×	22 0.6×	38	222
Bairen Yi Hong Kong	6	68 0.6×	122 1.2×	159 1.9×	228 3.7×	63 1.7×	6	397
Amedeo Sapio Italy	6	32 0.3×	39 0.4×	63 0.8×	250 4.1×	71 1.9×	12	304

Countries citing papers authored by Benoit Steiner

Since Specialization

Citations

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

Fields of papers citing papers by Benoit Steiner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benoit Steiner

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

All Works

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5 of 5 papers shown

1.

Singh, Shikhar, James Hegarty, Hugh Leather, & Benoit Steiner. (2022). A graph neural network-based performance model for deep learning applications. 11–20. 3 indexed citations

2.

Steiner, Benoit, Chris Cummins, Horace He, & Hugh Leather. (2021). Value Learning for Throughput Optimization of Deep Learning Workloads. 3. 323–334. 17 indexed citations

3.

Adams, Andrew, Luke Anderson, Riyadh Baghdadi, et al.. (2019). Learning to optimize halide with tree search and random programs. ACM Transactions on Graphics. 38(4). 1–12. 129 indexed citations

4.

Mirhoseini, Azalia, Anna Goldie, Hieu Pham, et al.. (2018). A Hierarchical Model for Device Placement. International Conference on Learning Representations. 55 indexed citations

5.

Mirhoseini, Azalia, et al.. (2018). Hierarchical Planning for Device Placement. 9 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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