Zobrazeno 1 - 10
of 211
pro vyhledávání: '"MORIMOTO,Yasuhiko"'
Federated learning (FL) has emerged as a transformative training paradigm, particularly invaluable in privacy-sensitive domains like healthcare. However, client heterogeneity in data, computing power, and tasks poses a significant challenge. To addre
Externí odkaz:
http://arxiv.org/abs/2409.19741
Deep generative models, such as generative adversarial networks (GANs), are pivotal in discovering novel drug-like candidates via de novo molecular generation. However, traditional character-wise tokenizers often struggle with identifying novel and c
Externí odkaz:
http://arxiv.org/abs/2409.19740
Aspect-based sentiment analysis predicts sentiment polarity with fine granularity. While graph convolutional networks (GCNs) are widely utilized for sentimental feature extraction, their naive application for syntactic feature extraction can compromi
Externí odkaz:
http://arxiv.org/abs/2404.03259
The computation of the skyline provides a mechanism for utilizing multiple location-based criteria to identify optimal data points. However, the efficiency of these computations diminishes and becomes more challenging as the input data expands. This
Externí odkaz:
http://arxiv.org/abs/2404.03254
Deep generative models, such as generative adversarial networks (GANs), have been employed for $de~novo$ molecular generation in drug discovery. Most prior studies have utilized reinforcement learning (RL) algorithms, particularly Monte Carlo tree se
Externí odkaz:
http://arxiv.org/abs/2404.00081
A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training
The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers. Since the BERT typically acts as an end-to-end black box, classification systems based on it usually hav
Externí odkaz:
http://arxiv.org/abs/2309.01196
Publikováno v:
Machine Intelligence Research, 2024 (https://link.springer.com/article/10.1007/s11633-023-1440-x)
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy. This paper introduces an end-to-end deep learning model, named RKGCN,
Externí odkaz:
http://arxiv.org/abs/2305.01147
Usually, a medical record (MR) contains the patients disease-oriented sensitive information. In addition, the MR needs to be shared among different bodies, e.g., diagnostic centres, hospitals, physicians, etc. Hence, retaining the privacy and integri
Externí odkaz:
http://arxiv.org/abs/2304.13511
Autor:
Li, Chen a, ⁎, Zhu, Ye b, Cao, Yang b, Zhang, Jinli c, Annisa, Annisa d, Cheng, Debo e, Morimoto, Yasuhiko f
Publikováno v:
In Array March 2025 25
Autor:
Tang, Huidong, Li, Chen, Jiang, Shuai, Yu, Huachong, Kamei, Sayaka, Yamanishi, Yoshihiro, Morimoto, Yasuhiko
Publikováno v:
In Pattern Recognition Letters November 2023 175:45-51