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Organization / Workplace
Bunkyo-ku, Tokyo, Japan
Occupation
准教授
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Education

Presentations(16) 

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[ICLR2021 (spotlight)] Benefit of deep learning with non-convex noisy gradient descent
[ICLR2021 (spotlight)] Benefit of deep learning with non-convex noisy gradient descent[ICLR2021 (spotlight)] Benefit of deep learning with non-convex noisy gradient descent
[ICLR2021 (spotlight)] Benefit of deep learning with non-convex noisy gradient descent
 
[NeurIPS2020 (spotlight)] Generalization bound of globally optimal non convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
[NeurIPS2020 (spotlight)] Generalization bound of globally optimal non convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics[NeurIPS2020 (spotlight)] Generalization bound of globally optimal non convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
[NeurIPS2020 (spotlight)] Generalization bound of globally optimal non convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
 
深層学習の数理：カーネル法, スパース推定との接点
深層学習の数理：カーネル法, スパース推定との接点深層学習の数理：カーネル法, スパース推定との接点
深層学習の数理：カーネル法, スパース推定との接点
 
Iclr2020: Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
Iclr2020: Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural networkIclr2020: Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
Iclr2020: Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
 
数学で解き明かす深層学習の原理
数学で解き明かす深層学習の原理数学で解き明かす深層学習の原理
数学で解き明かす深層学習の原理
 
深層学習の数理
深層学習の数理深層学習の数理
深層学習の数理
 
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