1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model
Qianren Mao, Yashuo Luo, Ziqi Qin +12
The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effect…
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism
Jia-Hao Syu, Jerry Chun-Wei Lin, Gautam Srivastava +1
Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict p…
Distributed Multi-Head Learning Systems for Power Consumption Prediction
Jia-Hao Syu, Jerry Chun-Wei Lin, Philip S. Yu
As more and more automatic vehicles, power consumption prediction becomes a vital issue for task scheduling and energy management. Most research focuses on automatic vehicles in tr…
Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction
Jia-Hao Syu, Jerry Chun-Wei Lin
In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm wit…