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ORCIDYoshitomo Matsubara
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2020 – today
- 2023
[j3]Yoshitomo Matsubara
, Marco Levorato, Francesco Restuccia:
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges. ACM Comput. Surv. 55(5): 90:1-90:30 (2023)- [j2]Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
SC2 Benchmark: Supervised Compression for Split Computing. Trans. Mach. Learn. Res. 2023 (2023) - [c12]Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha, Alessandro Moschitti:
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages. ACL (Findings) 2023: 14078-14092 - [c11]Niloofar Bahadori, Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia:
SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing. ICDCS 2023: 864-874 - [i13]Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha, Alessandro Moschitti:
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages. CoRR abs/2305.16302 (2023) - [i12]Niloofar Bahadori, Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia:
SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing. CoRR abs/2310.08656 (2023) - [i11]Yoshitomo Matsubara:
torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP. CoRR abs/2310.17644 (2023) - [i10]Florian Lalande, Yoshitomo Matsubara, Naoya Chiba, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku:
A Transformer Model for Symbolic Regression towards Scientific Discovery. CoRR abs/2312.04070 (2023) - 2022
- [b1]Yoshitomo Matsubara:
Towards Split Computing: Supervised Compression for Resource-Constrained Edge Computing Systems. University of California, Irvine, USA, 2022 - [c10]Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti:
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems. EMNLP (Findings) 2022: 7259-7272 - [c9]Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
Supervised Compression for Resource-Constrained Edge Computing Systems. WACV 2022: 923-933 - [c8]Yoshitomo Matsubara, Davide Callegaro, Sameer Singh, Marco Levorato, Francesco Restuccia:
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing. WoWMoM 2022: 337-346 - [i9]Yoshitomo Matsubara, Davide Callegaro, Sameer Singh, Marco Levorato, Francesco Restuccia:
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing. CoRR abs/2201.02693 (2022) - [i8]Yoshitomo Matsubara, Luca Soldaini, Eric Lind, Alessandro Moschitti:
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems. CoRR abs/2201.05767 (2022) - [i7]Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
SC2: Supervised Compression for Split Computing. CoRR abs/2203.08875 (2022) - [i6]Yoshitomo Matsubara, Naoya Chiba, Ryo Igarashi, Tatsunori Taniai, Yoshitaka Ushiku:
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery. CoRR abs/2206.10540 (2022) - 2021
- [c7]Yoshitomo Matsubara:
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation. RRPR 2021: 24-44 - [i5]Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia:
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges. CoRR abs/2103.04505 (2021) - [i4]Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt:
Supervised Compression for Resource-constrained Edge Computing Systems. CoRR abs/2108.11898 (2021) - 2020
- [j1]Yoshitomo Matsubara, Davide Callegaro, Sabur Baidya, Marco Levorato, Sameer Singh:
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems. IEEE Access 8: 212177-212193 (2020) - [c6]Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, Sameer Singh:
COVIDLies: Detecting COVID-19 Misinformation on Social Media. NLP4COVID@EMNLP 2020 - [c5]Davide Callegaro, Yoshitomo Matsubara, Marco Levorato:
Optimal Task Allocation for Time-Varying Edge Computing Systems with Split DNNs. GLOBECOM 2020: 1-6 - [c4]Yoshitomo Matsubara, Marco Levorato:
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks. ICPR 2020: 2272-2279 - [c3]Yoshitomo Matsubara, Thuy Vu, Alessandro Moschitti:
Reranking for Efficient Transformer-based Answer Selection. SIGIR 2020: 1577-1580 - [i3]Yoshitomo Matsubara, Marco Levorato:
Split Computing for Complex Object Detectors: Challenges and Preliminary Results. CoRR abs/2007.13312 (2020) - [i2]Yoshitomo Matsubara, Marco Levorato:
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks. CoRR abs/2007.15818 (2020) - [i1]Yoshitomo Matsubara:
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation. CoRR abs/2011.12913 (2020)
2010 – 2019
- 2019
- [c2]Yoshitomo Matsubara, Sabur Baidya, Davide Callegaro, Marco Levorato, Sameer Singh:
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems. HotEdgeVideo@MobiCom 2019: 21-26 - 2016
- [c1]Yoshitomo Matsubara, Haruhiko Nishimura, Toshiharu Samura, Hiroyuki Yoshimoto, Ryohei Tanimoto:
Screen Unlocking by Spontaneous Flick Reactions with One-Class Classification Approaches. ICMLA 2016: 752-757
Coauthor Index
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