no code implementations • 26 Mar 2024 • Yusuke Takimoto, Hikari Takehara, Hiroyuki Sato, Zihao Zhu, Bo Zheng
In the film and gaming industries, achieving a realistic hair appearance typically involves the use of strands originating from the scalp.
no code implementations • 19 Feb 2024 • Hiroyuki Sato, Keisuke Suzuki, Atsushi Hashizume, Ryoichi Hanazawa, Masanao Sasaki, Akihiro Hirakawa, the Japanese Alzheimer's Disease Neuroimaging Initiative, the Alzheimer's Disease Neuroimaging Initiative
Various predictive models have been designed to realize its early onset and study the long-term trajectories of cognitive test scores across populations of interest.
no code implementations • 30 Jan 2024 • Zhennan Wu, Yang Li, Han Yan, Taizhang Shang, Weixuan Sun, Senbo Wang, Ruikai Cui, Weizhe Liu, Hiroyuki Sato, Hongdong Li, Pan Ji
A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed.
no code implementations • 4 Apr 2022 • Yusuke Takimoto, Hiroyuki Sato, Hikari Takehara, Keishiro Uragaki, Takehiro Tawara, Xiao Liang, Kentaro Oku, Wataru Kishimoto, Bo Zheng
HardSoftRas, our novel rendering process, is designed for inverse rendering with a graphics pipeline.
1 code implementation • ICML 2018 • Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra
Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite number of loss functions on a Riemannian manifold.
no code implementations • 15 Mar 2017 • Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra
The present paper proposes a Riemannian stochastic quasi-Newton algorithm with variance reduction (R-SQN-VR).
1 code implementation • 18 Feb 2017 • Hiroyuki Sato, Hiroyuki Kasai, Bamdev Mishra
In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions.
1 code implementation • 24 May 2016 • Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra
In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space.