2 citations · 3 across the 5 of their papers we have counts for
5 papers
A Reinforcement Learning-Driven Transformer GAN for Molecular Generation
Chen Li, Huidong Tang, Ye Zhu +1
Generating molecules with desired chemical properties presents a critical challenge in fields such as chemical synthesis and drug discovery. Recent advancements in artificial intel…
Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation
Huidong Tang, Chen Li, Huachong Yu +2
Federated learning (FL) has emerged as a transformative training paradigm, particularly invaluable in privacy-sensitive domains like healthcare. However, client heterogeneity in da…
When Molecular GAN Meets Byte-Pair Encoding
Huidong Tang, Chen Li, Yasuhiko Morimoto
Deep generative models, such as generative adversarial networks (GANs), are pivotal in discovering novel drug-like candidates via de novo molecular generation. However, traditional…
Advancing Aspect-Based Sentiment Analysis through Deep Learning Models
Chen Li, Huidong Tang, Jinli Zhang +3
Aspect-based sentiment analysis predicts sentiment polarity with fine granularity. While graph convolutional networks (GCNs) are widely utilized for sentimental feature extraction,…
Molecular Generative Adversarial Network with Multi-Property Optimization
Huidong Tang, Chen Li, Sayaka Kamei +2
Deep generative models, such as generative adversarial networks (GANs), have been employed for molecular generation in drug discovery. Most prior studies have utilized re…