3 papers
cs.LG2025
Supervised Graph Contrastive Learning for Gene Regulatory Networks
Sho Oshima, Yuji Okamoto, Taisei Tosaki +1
Graph Contrastive Learning (GCL) is a powerful self-supervised learning framework that performs data augmentation through graph perturbations, with growing applications in the anal…
cs.LG2024
Learning Deep Dissipative Dynamics
Yuji Okamoto, Ryosuke Kojima
This study challenges strictly guaranteeing ``dissipativity'' of a dynamical system represented by neural networks learned from given time-series data. Dissipativity is a crucial i…
cs.LG2023
A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data
Aya Nakamura, Ryosuke Kojima, Yuji Okamoto +7
Many diseases, including cancer and chronic conditions, require extended treatment periods and long-term strategies. Machine learning and AI research focusing on electronic health…