1 citations · 1 across the 5 of their papers we have counts for
5 papers
Multi-modal cascade feature transfer for polymer property prediction
Kiichi Obuchi, Yuta Yahagi, Kiyohiko Toyama +2
In this paper, we propose a novel transfer learning approach called multi-modal cascade model with feature transfer for polymer property prediction.Polymers are characterized by a…
Dose-finding design based on level set estimation in phase I cancer clinical trials
Keiichiro Seno, Kota Matsui, Shogo Iwazaki +3
The primary objective of phase I cancer clinical trials is to evaluate the safety of a new experimental treatment and to find the maximum tolerated dose (MTD). We show that the MTD…
Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation
Yuta Yahagi, Kiichi Obuchi, Fumihiko Kosaka +1
Simulation-to-Real (Sim2Real) transfer learning, the machine learning technique that efficiently solves a real-world task by leveraging knowledge from computational data, has recei…
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation
Shota Hozumi, Kentaro Kutsukake, Kota Matsui +3
In material characterization, identifying defective areas on a material surface is fundamental. The conventional approach involves measuring the relevant physical properties point-…
Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle
Kota Matsui, Wataru Kumagai, Takafumi Kanamori
This paper provides a block coordinate descent algorithm to solve unconstrained optimization problems. In our algorithm, computation of function values or gradients is not required…