4 citations · 11 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
MTA: A Merge-then-Adapt Framework for Personalized Large Language Model
Xiaopeng Li, Yuanjin Zheng, Wanyu Wang +6
Personalized Large Language Models (PLLMs) aim to align model outputs with individual user preferences, a crucial capability for user-centric applications. However, the prevalent a…
Boosting Fine-Grained Urban Flow Inference via Lightweight Architecture and Focalized Optimization
Yuanshao Zhu, Xiangyu Zhao, Zijian Zhang +2
Fine-grained urban flow inference is crucial for urban planning and intelligent transportation systems, enabling precise traffic management and resource allocation. However, the pr…
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…
Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
Langming Liu, Wanyu Wang, Chi Zhang +6
Online advertising in recommendation platforms has gained significant attention, with a predominant focus on channel recommendation and budget allocation strategies. However, curre…
Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision
Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao +4
The widespread adoption of mobile devices and data collection technologies has led to an exponential increase in trajectory data, presenting significant challenges in spatio-tempor…
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…
Sliding Window Attention Training for Efficient Large Language Models
Zichuan Fu, Wentao Song, Yejing Wang +7
Recent advances in transformer-based Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their quadratic computational complexity…