40 citations · 51 across the 8 of their papers we have counts for
8 papers
Flexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training
Sota Kudo, Naoaki Ono, Shigehiko Kanaya +1
Information Bottleneck (IB) is a widely used framework that enables the extraction of information related to a target random variable from a source random variable. In the objectiv…
Pre-training of Molecular GNNs via Conditional Boltzmann Generator
Daiki Koge, Naoaki Ono, Shigehiko Kanaya
Learning representations of molecular structures using deep learning is a fundamental problem in molecular property prediction tasks. Molecules inherently exist in the real world a…
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model
Daiki Koge, Naoaki Ono, Shigehiko Kanaya
In data-driven drug discovery, designing molecular descriptors is a very important task. Deep generative models such as variational autoencoders (VAEs) offer a potential solution b…
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data
Ziwei Yang, Lingwei Zhu, Zheng Chen +4
Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. Promising results on…
Automated Sleep Staging via Parallel Frequency-Cut Attention
Zheng Chen, Ziwei Yang, Lingwei Zhu +6
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep me…
A Gap in the Community-Size Distribution of a Large-Scale Social Networking Site
Kikuo Yuta, Naoaki Ono, Yoshi Fujiwara
Social networking sites (SNS) have recently used by millions of people all over the world. An SNS is a society on the Internet, where people communicate and foster friendship with…