most citedCLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey

2 citations · 6 across the 9 of their papers we have counts for

collaborators

13 papers

cs.IR20261 cited

SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation

Chunxu Zhang, Shanqiang Huang, Zijian Zhang +5

Cross-domain recommendation (CDR) addresses the data sparsity and cold-start problems in the target domain by leveraging knowledge from data-rich source domains. However, existing…

cs.ET2026

UrbanMoE: A Sparse Multi-Modal Mixture-of-Experts Framework for Multi-Task Urban Region Profiling

Pingping Liu, Jiamiao Liu, Zijian Zhang +5

Urban region profiling, the task of characterizing geographical areas, is crucial for urban planning and resource allocation. However, existing research in this domain faces two si…

cs.LG2026

Semi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphs

Zixing Song, Irwin King

The emergent reasoning capabilities of Large Language Models (LLMs) offer a transformative paradigm for analyzing text-attributed graphs. While instruction tuning is the prevailing…

cond-mat.mtrl-sci20261 cited

Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

Haowei Hua, Chen Liang, Ding Pan +4

Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such…

cs.SD2026

Towards Practical Automatic Piano Reduction using BERT with Semi-supervised Learning

Wan Ki Wong, Ka Ho To, Chuck-jee Chau +3

In this study, we present a novel automatic piano reduction method with semi-supervised machine learning. Piano reduction is an important music transformation process, which helps…

cs.CL20251 cited

Implicit Reasoning in Large Language Models: A Comprehensive Survey

Jindong Li, Yali Fu, Li Fan +6

Large Language Models (LLMs) have demonstrated strong generalization across a wide range of tasks. Reasoning with LLMs is central to solving multi-step problems and complex decisio…