1 citations · 1 across the 4 of their papers we have counts for
4 papers
TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
Qingyun Liu, Bo Yan, Yang Liu +16
User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints due to fixed-dimensional vectors. As an emerging…
Wisdom of Committee: Diverse Distillation from Large Foundation Models and Domain Experts
Zichang Liu, Qingyun Liu, Yuening Li +6
Knowledge distillation from foundation models to compact domain models is challenging due to substantial gaps in capacity, architecture, and modality. For example, in our experimen…
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
Yuji Roh, Qingyun Liu, Huan Gui +8
Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various task…
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication
Zhe Zhao, Qingyun Liu, Huan Gui +3
Many recent breakthroughs in machine learning have been enabled by the pre-trained foundation models. By scaling up model parameters, training data, and computation resources, foun…