Yoshitomo Matsubara

912 total citations · 1 hit paper
17 papers, 486 citations indexed

About

Yoshitomo Matsubara is a scholar working on Information Systems, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yoshitomo Matsubara has authored 17 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Information Systems, 6 papers in Computer Networks and Communications and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Yoshitomo Matsubara's work include User Authentication and Security Systems (5 papers), IoT and Edge/Fog Computing (4 papers) and Biometric Identification and Security (4 papers). Yoshitomo Matsubara is often cited by papers focused on User Authentication and Security Systems (5 papers), IoT and Edge/Fog Computing (4 papers) and Biometric Identification and Security (4 papers). Yoshitomo Matsubara collaborates with scholars based in United States, Japan and Italy. Yoshitomo Matsubara's co-authors include Marco Levorato, Francesco Restuccia, Sameer Singh, Sean D. Young, Robert L. Logan, Sabur Baidya, Ruihan Yang, Stephan Mandt, Alessandro Moschitti and Thuy Vu and has published in prestigious journals such as IEEE Access, ACM Computing Surveys and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

In The Last Decade

Yoshitomo Matsubara

17 papers receiving 475 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.

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

2022 145 citations Yoshitomo Matsubara, Marco Levorato et al. ACM Computing Surveys profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yoshitomo Matsubara United States	6	256	163	133	102	83	17	486
Yutao Huang China	8	488 1.9×	95 0.6×	180 1.4×	31 0.3×	112 1.3×	14	689
Xiaofei Zhu China	14	249 1.0×	115 0.7×	68 0.5×	80 0.8×	156 1.9×	62	647
Varun Chandrasekaran United States	7	331 1.3×	99 0.6×	56 0.4×	29 0.3×	66 0.8×	18	447
Nathan Dowlin United States	3	735 2.9×	181 1.1×	62 0.5×	24 0.2×	129 1.6×	4	827
Chengcheng Li United States	9	250 1.0×	106 0.7×	64 0.5×	31 0.3×	55 0.7×	33	391
Alessandro Renda Italy	10	232 0.9×	50 0.3×	38 0.3×	54 0.5×	48 0.6×	24	382
Ya-Qin Zhang China	13	183 0.7×	176 1.1×	137 1.0×	27 0.3×	35 0.4×	32	480
Jawad Khan South Korea	11	219 0.9×	134 0.8×	57 0.4×	18 0.2×	71 0.9×	32	500
El Mahdi El Mhamdi Switzerland	8	666 2.6×	40 0.2×	144 1.1×	27 0.3×	99 1.2×	19	757
Samer Atawneh Saudi Arabia	13	195 0.8×	168 1.0×	155 1.2×	32 0.3×	230 2.8×	25	638

Countries citing papers authored by Yoshitomo Matsubara

Since Specialization

Citations

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

Fields of papers citing papers by Yoshitomo Matsubara

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yoshitomo Matsubara

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

All Works

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17 of 17 papers shown

Matsubara, Yoshitomo, et al.. (2025). A Multi-Task Supervised Compression Model for Split Computing. 4913–4922. 1 indexed citations

Matsubara, Yoshitomo, et al.. (2023). Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages. 14078–14092. 3 indexed citations

Matsubara, Yoshitomo, et al.. (2023). SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing. 864–874. 1 indexed citations

Matsubara, Yoshitomo. (2023). torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP. 153–164. 1 indexed citations

Matsubara, Yoshitomo, Luca Soldaini, Eric M. Lind, & Alessandro Moschitti. (2022). Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems. 7259–7272. 5 indexed citations

Matsubara, Yoshitomo, Marco Levorato, & Francesco Restuccia. (2022). Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges. ACM Computing Surveys. 55(5). 1–30. 145 indexed citations breakdown →

Matsubara, Yoshitomo, Ruihan Yang, Marco Levorato, & Stephan Mandt. (2022). Supervised Compression for Resource-Constrained Edge Computing Systems. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 923–933. 42 indexed citations

Matsubara, Yoshitomo, et al.. (2020). Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems. IEEE Access. 8. 212177–212193. 46 indexed citations

Matsubara, Yoshitomo, Thuy Vu, & Alessandro Moschitti. (2020). Reranking for Efficient Transformer-based Answer Selection. 1577–1580. 27 indexed citations

10.

Logan, Robert L., et al.. (2020). COVIDLies: Detecting COVID-19 Misinformation on Social Media. 137 indexed citations

11.

Matsubara, Yoshitomo, et al.. (2020). Optimal Task Allocation for Time-Varying Edge Computing Systems with Split DNNs. 1–6. 2 indexed citations

12.

Matsubara, Yoshitomo, et al.. (2019). Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems. 21–26. 62 indexed citations

13.

Matsubara, Yoshitomo, et al.. (2016). Screen Unlocking by Spontaneous Flick Reactions with One-Class Classification Approaches. 8. 752–757. 1 indexed citations

14.

Matsubara, Yoshitomo, et al.. (2015). Keyboard Dependency of Personal Identification Performance by Keystroke Dynamics in Free Text Typing. Journal of Information Security. 6(3). 229–240. 5 indexed citations

15.

Matsubara, Yoshitomo, et al.. (2015). A New Biometrics Technique with Flick Operation on Electronic Device. IEICE Technical Report; IEICE Tech. Rep.. 115(266). 91–96. 1 indexed citations

16.

Matsubara, Yoshitomo, et al.. (2015). Profile Generation Methods for Reinforcing Robustness of Keystroke Authentication in Free Text Typing. Journal of Information Security. 6(2). 131–141. 2 indexed citations

17.

Matsubara, Yoshitomo, et al.. (2013). Performance assessment in keystroke dynamics by combined profile documents for free text typing. Society of Instrument and Control Engineers of Japan. 265–270. 5 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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