慶應義塾大学 理工学部 矢上キャンパス 14棟 631A/B室
〒223-8522 横浜市港北区日吉3-14-1
Speaker:Dr. Koichi Taniguchi (Nagoya University)
Title: On approximation ability for Besov space by deep ReLU network
Abstract: Recently, it has been shown that deep learning outperforms any
linear estimator such as kernel ridge regression, where the
target function has highly spatial inhomogeneity of its
smoothness. To generalize these results, we consider the
approximation and estimation error bounds of deep ReLU networks
where the target function is in a Besov space with variable
smoothness. The Besov space is a function space with three
exponents (i.e., smoothness exponent, integrability exponent
and interpolation exponent), which includes H\"older space
and Sobolev space. This talk is based on the joint work with
Sho Sonoda (RIKEN), Masahiro Ikeda (RIKEN/ Keio Univ.),
Kenta Oono (Tokyo Univ./Preferred Networks), and
Taiji Suzuki (Tokyo Univ./RIKEN).
