Scott Fujimoto

3.8k citations
6 papers · 191 indexed · h-index 3
Journals
UvA-DARE (University of Amsterdam) (1 paper)arXiv (Cornell University) (2 papers)International Conference on Machine Learning (1 paper)
Partner nations
CanadaNetherlandsSweden

In The Last Decade

Scott Fujimoto

4 papers receiving 184 citations

Peers

Scott Fujimoto
Comparison fields: 5 of 49
  • Artificial Intelligence 114
  • Control and Systems Engineering 51
  • Automotive Engineering 25
  • Computer Vision and Pattern Recognition 36
  • Computational Theory and Mathematics 23
Replace Junhyuk Oh with:
Junhyuk Oh United States
Matthieu Geist France
Prateek Singhal India
Kimin Lee South Korea
Gereon Weiß Germany
Ürün Doǧan United States
Guangsen Wang China
Fabio Pardo United Kingdom
Jacek M. Czerniak Poland
Izaskun Oregi Spain
Scott Fujimoto relative to Junhyuk Oh United States Junhyuk Oh's profile →
Citations per field
00.5×3.1×
Junhyuk Oh · 1×
Citations per year

Countries citing papers authored by Scott Fujimoto

Since Specialization
Citations

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

Fields of papers citing papers by Scott Fujimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 9 scholars most cited alongside Scott Fujimoto, 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 Scott Fujimoto Line = papers co-authored together Scott Fujimoto links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown
#Work
1 20250
2 20240
3 20211
4 201915
5
Addressing Function Approximation Error in Actor-Critic Methods
2018143
6 201832

About Scott Fujimoto

Scott Fujimoto is a scholar working on Computer Graphics and Computer-Aided Design, Artificial Intelligence and Management Science and Operations Research, having authored 6 papers that have together received 191 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (2 papers), Topic Modeling (1 paper), Advanced Text Analysis Techniques (1 paper), Age of Information Optimization (1 paper), Multimodal Machine Learning Applications (1 paper), Computational Geometry and Mesh Generation (1 paper), Evolutionary Algorithms and Applications (1 paper) and Computer Graphics and Visualization Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (114 citations), Control and Systems Engineering (51 citations) and Automotive Engineering (25 citations). Scott Fujimoto has collaborated with scholars based in Canada, Netherlands and Sweden. Frequent co-authors include David Meger, Herke van Hoof, Adriana Romero, Edward J. Smith, Eisha Ahmed, Derek Ruths, Gregory Dudek, Francois R. Hogan and Doina Precup. Their work appears in journals such as UvA-DARE (University of Amsterdam), arXiv (Cornell University) and International Conference on Machine Learning.

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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