2 citations · 2 across the 2 of their papers we have counts for
2 papers
stat.ML2021
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives
Hideyuki Tachibana, Mocho Go, Muneyoshi Inahara +2
Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of n…
cs.CL2020★ 2 cited
Accent Estimation of Japanese Words from Their Surfaces and Romanizations for Building Large Vocabulary Accent Dictionaries
Hideyuki Tachibana, Yotaro Katayama
In Japanese text-to-speech (TTS), it is necessary to add accent information to the input sentence. However, there are a limited number of publicly available accent dictionaries, an…