3 papers
cs.LG2025
AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
Haonan Dong, Wenhao Zhu, Guojie Song +1
Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method validated across NLP and CV domains. However, LoRA faces an inherent low-rank bottlenec…
cs.IR2025
CausalRec: A CausalBoost Attention Model for Sequential Recommendation
Yunbo Hou, Tianle Yang, Ruijie Li +5
Recent advances in correlation-based sequential recommendation systems have demonstrated substantial success. Specifically, the attention-based model outperforms other RNN-based an…
cs.LG2025
DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach
Qin Chen, Liang Wang, Bo Zheng +1
The pre-train then fine-tune approach has advanced GNNs by enabling general knowledge capture without task-specific labels. However, an objective gap between pre-training and downs…