1 citations · 1 across the 10 of their papers we have counts for
5 papers · 1 filter
Trustworthy Efficient Communication for Distributed Learning using LQ-SGD Algorithm
Hongyang Li, Lincen Bai, Caesar Wu +3
We propose LQ-SGD (Low-Rank Quantized Stochastic Gradient Descent), an efficient communication gradient compression algorithm designed for distributed training. LQ-SGD further deve…
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models
Jingjing Xu, Caesar Wu, Yuan-Fang Li +2
Transformer-based models for time series forecasting (TSF) have attracted significant attention in recent years due to their effectiveness and versatility. However, these models of…
Trustworthiness of Stochastic Gradient Descent in Distributed Learning
Hongyang Li, Caesar Wu, Mohammed Chadli +2
Distributed learning (DL) uses multiple nodes to accelerate training, enabling efficient optimization of large-scale models. Stochastic Gradient Descent (SGD), a key optimization a…
Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
Jingjing Xu, Caesar Wu, Yuan-Fang Li +2
Alongside the continuous process of improving AI performance through the development of more sophisticated models, researchers have also focused their attention to the emerging con…
Transformer Multivariate Forecasting: Less is More?
Jingjing Xu, Caesar Wu, Yuan-Fang Li +1
In the domain of multivariate forecasting, transformer models stand out as powerful apparatus, displaying exceptional capabilities in handling messy datasets from real-world contex…