124 citations · 197 across the 8 of their papers we have counts for
8 papers
Stoichiometric growth of SrTiO3 films via Bayesian optimization with adaptive prior mean
Yuki K. Wakabayashi, Takuma Otsuka, Yoshiharu Krockenberger +3
Perovskite insulator SrTiO3 is expected to be applied to the next generation of electronic and photonic devices as high-k capacitors and photocatalysts. However, reproducible growt…
Bayesian optimization with experimental failure for high-throughput materials growth
Yuki K. Wakabayashi, Takuma Otsuka, Yoshiharu Krockenberger +3
A crucial problem in achieving innovative high-throughput materials growth with machine learning and automation techniques, such as Bayesian optimization (BO) and robotic experimen…
Quantum transport evidence of Weyl fermions in an epitaxial ferromagnetic oxide
Kosuke Takiguchi, Yuki K. Wakabayashi, Hiroshi Irie +8
Magnetic Weyl fermions, which occur in magnets, have novel transport phenomena related to pairs of Weyl nodes, and they are, of both, scientific and technological interest, with th…
Efficient Transfer Bayesian Optimization with Auxiliary Information
Tomoharu Iwata, Takuma Otsuka
We propose an efficient transfer Bayesian optimization method, which finds the maximum of an expensive-to-evaluate black-box function by using data on related optimization tasks. O…
Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films
Yuki K. Wakabayashi, Takuma Otsuka, Yoshiharu Krockenberger +3
Materials informatics exploiting machine learning techniques, e.g., Bayesian optimization (BO), has the potential to offer high-throughput optimization of thin-film growth conditio…
On Transformations in Stochastic Gradient MCMC
Soma Yokoi, Takuma Otsuka, Issei Sato
Stochastic gradient Langevin dynamics (SGLD) is a computationally efficient sampler for Bayesian posterior inference given a large scale dataset. Although SGLD is designed for unbo…
Finding Appropriate Traffic Regulations via Graph Convolutional Networks
Tomoharu Iwata, Takuma Otsuka, Hitoshi Shimizu +3
Appropriate traffic regulations, e.g. planned road closure, are important in congested events. Crowd simulators have been used to find appropriate regulations by simulating multipl…
Multi-output Polynomial Networks and Factorization Machines
Mathieu Blondel, Vlad Niculae, Takuma Otsuka +1
Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these models to the multi-output setting, i.…