no code implementations • 9 Mar 2024 • Ming Zheng, Yang Yang, Zhi-Hang Zhao, Shan-Chao Gan, Yang Chen, Si-Kai Ni, Yang Lu
In the current oversampling methods based on generative networks, the methods based on GANs can capture the true distribution of data, but there is the problem of pattern collapse and training instability in training; in the oversampling methods based on denoising diffusion probability models, the neural network of the inverse diffusion process using the U-Net is not applicable to tabular data, and although the MLP can be used to replace the U-Net, the problem exists due to the simplicity of the structure and the poor effect of removing noise.
no code implementations • 19 Jan 2021 • Weilu Gao, Davoud Adinehloo, Ali Mojibpour, Yohei Yomogida, Atsushi Hirano, Takeshi Tanaka, Hiromichi Kataura, Ming Zheng, Vasili Perebeinos, Junichiro Kono
Significant understanding has been achieved over the last few decades regarding chirality-dependent properties of single-wall carbon nanotubes (SWCNTs), primarily through single-tube studies.
Mesoscale and Nanoscale Physics
no code implementations • 23 Dec 2018 • Dianbo Liu, Nestor Sepulveda, Ming Zheng
In this project we explored methods to increase computational efficiency of ML algorithms, in particular Artificial Neural Nets (NN), while not compromising the accuracy of the predicted results.