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)
Research Achievements
Related posts
posted on October 15, 2024 14:17Seminar
posted on May 15, 2024 17:34Information
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posted on November 6, 2023 22:01Seminar
posted on October 12, 2023 18:23Seminar
posted on September 25, 2023 20:05Information
posted on May 8, 2023 15:05Information
posted on May 2, 2023 09:42Information
posted on January 27, 2023 20:39Information
posted on September 27, 2022 19:29Information
posted on May 23, 2022 10:51Information
posted on April 6, 2022 17:39Seminar
posted on January 26, 2022 19:35Information
posted on January 24, 2022 08:52Information
posted on October 14, 2021 12:38Information
posted on October 8, 2021 16:33Information
posted on July 26, 2021 15:26Information
posted on July 7, 2021 13:00Seminar
posted on May 21, 2021 15:49Information
posted on May 18, 2021 07:49Information
posted on April 6, 2021 08:52Award
posted on March 15, 2021 12:59Seminar
posted on February 9, 2021 14:54Seminar
posted on January 29, 2021 13:48Information
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posted on October 2, 2020 09:00Information
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posted on March 13, 2020 11:20Information
posted on March 13, 2020 11:25Information
posted on September 11, 2019 12:00Information
posted on May 16, 2019 13:52Information
posted on September 7, 2018 17:55Information
