1 citations · 1 across the 4 of their papers we have counts for
5 papers
SmartGR: Hierarchy and Beam-Aware Knowledge Distillation for Generative Recommendation
Ziheng Zhang, Yu Cui, Bohao Wang +6
Generative recommendation (GR) has emerged as a promising paradigm for recommender systems. Scaling up GR models can improve recommendation performance, but it also substantially i…
TopKGAT: A Top-K Objective-Driven Architecture for Recommendation
Sirui Chen, Jiawei Chen, Canghong Jin +4
Recommendation systems (RS) aim to retrieve the top-K items most relevant to users, with metrics such as Precision@K and Recall@K commonly used to assess effectiveness. The archite…
Knowledge Distillation with Refined Logits
Wujie Sun, Defang Chen, Siwei Lyu +3
Recent research on knowledge distillation has increasingly focused on logit distillation because of its simplicity, effectiveness, and versatility in model compression. In this pap…
Knowledge Translation: A New Pathway for Model Compression
Wujie Sun, Defang Chen, Jiawei Chen +3
Deep learning has witnessed significant advancements in recent years at the cost of increasing training, inference, and model storage overhead. While existing model compression met…
Accelerating Diffusion Sampling with Classifier-based Feature Distillation
Wujie Sun, Defang Chen, Can Wang +3
Although diffusion model has shown great potential for generating higher quality images than GANs, slow sampling speed hinders its wide application in practice. Progressive distill…