5 citations · 10 across the 5 of their papers we have counts for
5 papers
Fast Generalization after Interpolation via Critically Damped Momentum Optimization
Luca Muscarnera, Silas Ruhrberg Estévez, Yuanzhang Xiao +1
A central problem in machine learning is that models can achieve near-perfect training performance while generalizing substantially less well to unseen examples. This gap is especi…
Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning
Guangfeng Yan, Tan Li, Yuanzhang Xiao +2
Gradient compression has surfaced as a key technique to address the challenge of communication efficiency in distributed learning. In distributed deep learning, however, it is obse…
Truncated Non-Uniform Quantization for Distributed SGD
Guangfeng Yan, Tan Li, Yuanzhang Xiao +2
To address the communication bottleneck challenge in distributed learning, our work introduces a novel two-stage quantization strategy designed to enhance the communication efficie…
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
Sichun Luo, Yuxuan Yao, Bowei He +9
Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior. Recently, large language models (LL…
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Sichun Luo, Bowei He, Haohan Zhao +9
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has…