4 papers
Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics
Cong Xu, Wenbin Liang, Mo Yu +7
The rapid scaling of models has led to prohibitively high training and fine-tuning costs. A major factor accounting for memory consumption is the widespread use of stateful optimiz…
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
Cong Xu, Jun Wang, Jianyong Wang +1
Embedding plays a key role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision-making models. I…
Are LLM-based Recommenders Already the Best? Simple Scaled Cross-entropy Unleashes the Potential of Traditional Sequential Recommenders
Cong Xu, Zhangchi Zhu, Mo Yu +3
Large language models (LLMs) have been garnering increasing attention in the recommendation community. Some studies have observed that LLMs, when fine-tuned by the cross-entropy (C…
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Jiajun Cui, Minghe Yu, Bo Jiang +3
Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their futur…