7 papers
Renormalization Group Guided Tensor Network Structure Search
Maolin Wang, Bowen Yu, Sheng Zhang +8
Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional dat…
SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation
Binhao Wang, Yutian Xiao, Maolin Wang +4
Knowledge Graphs (KGs) enhance recommender systems but face challenges from inherent noise, sparsity, and Euclidean geometry's inadequacy for complex relational structures, critica…
DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation
Maolin Wang, Tianshuo Wei, Sheng Zhang +6
Neural Architecture Search (NAS) has emerged as a powerful approach for automating neural network design. However, existing NAS methods face critical limitations in real-world depl…
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
Maolin Wang, Yutian Xiao, Binhao Wang +6
Modern recommendation systems face significant challenges in processing multimodal sequential data, particularly in temporal dynamics modeling and information flow coordination. Tr…
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation
Maolin Wang, Sheng Zhang, Ruocheng Guo +6
Recent deep sequential recommendation models often struggle to effectively model key characteristics of user behaviors, particularly in handling sequence length variations and capt…
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning
Maolin Wang, Xiangyu Zhao
There has been a significant increase in the deployment of neural network models, presenting substantial challenges in model adaptation and fine-tuning. Efficient adaptation is cru…