Taiji Suzuki (Ph.D.)
Title
Team Leader
Members
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Team leaderTaiji Suzuki
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Senior research scientistSho Sonoda
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Research scientistWei Huang
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Postdoctoral researcherStefano Massaroli
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Visiting scientistTakafumi Kanamori
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Visiting scientistTakashi Takenouchi
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Visiting scientistHironori Fujisawa
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Visiting scientistShotaro Akaho
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Visiting scientistTakayuki Kawashima
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Visiting scientistYuichiro Wada
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Visiting scientistTomoya Murata
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Visiting scientistNoboru Murata
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Student traineeKazusato Oko
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Student traineeDragos Secrieru
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Part-time worker IKosaku Takanashi
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Part-time worker INaoki Nishikawa
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Part-time worker IIFumiya Uchiyama
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Part-time worker IIJuno Kim
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Part-time worker IIRyotaro Kawata
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Part-time worker IISyunta Seki
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Part-time worker IIRei Higuchi
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Part-time worker IIYujin Song
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Part-time worker IIIriiya Horiguchi
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Part-time worker IIKosei Matsutani
Introduction
Our team, deep learning theory team, is studying various kinds of learning systems including deep learning from theoretical viewpoints. We enrich our understandings of complex learning systems, and leverage the insights to construct new machine learning techniques and apply them. Especially, machine learning should deal with high dimensional and complicated data, and thus we are studying deep learning and structured sparse learning as methods to deal with such complicated data. Moreover, we are also developing efficient optimization algorithms for large and complicated machine learning problems based on such techniques as stochastic optimization.
Main Research Field
Computer Science
Research Field
Mathematics
Research Subjects
Statistical learning theory of wide range of learning systems including deep learning
Efficient optimization algorithm for large dataset
High dimensional statistics
Efficient optimization algorithm for large dataset
High dimensional statistics
RIKEN Website URL
Introduction Video
Poster(s)
- FY2023 Research Results(PDF 2MB)(Japanese version)
- FY2022 Research Results(PDF 2.17MB)(Japanese version)
- FY2021 Research Results(PDF 1.93MB)(Japanese version)
- FY2019 Research Results (Japanese version)
- FY2018 Research Results (Japanese version)
Related posts
posted on October 15, 2024 14:17Seminar
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