56 citations · 137 across the 10 of their papers we have counts for
10 papers
Text-guided Diffusion Model for 3D Molecule Generation
Yanchen Luo, Junfeng Fang, Sihang Li +5
The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property va…
Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number
Chen-Lu Ding, Jiancan Wu, Wei Lin +3
We introduce a novel self-supervised deep clustering approach tailored for unstructured data without requiring prior knowledge of the number of clusters, termed Adaptive Self-super…
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
Shuyao Wang, Yongduo Sui, Jiancan Wu +2
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to p…
Large Language Model Can Interpret Latent Space of Sequential Recommender
Zhengyi Yang, Jiancan Wu, Yanchen Luo +5
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a c…
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Zhengyi Yang, Jiancan Wu, Zhicai Wang +3
Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studie…
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Chengpeng Li, Zhengyi Yang, Jizhi Zhang +4
Reinforcement learning (RL) has been widely applied in recommendation systems due to its potential in optimizing the long-term engagement of users. From the perspective of RL, reco…