211 citations · 1.2k across the 80 of their papers we have counts for
5 papers · 1 filter
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang +4
Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many real-world applications. The core issue in MTS forecasting is how to effectively model com…
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning
Linchao Zhu, Sercan O. Arik, Yi Yang +1
We propose a novel adaptive transfer learning framework, learning to transfer learn (L2TL), to improve performance on a target dataset by careful extraction of the related informat…
TripletGAN: Training Generative Model with Triplet Loss
Gongze Cao, Yezhou Yang, Jie Lei +3
As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the art…
On the Importance of Consistency in Training Deep Neural Networks
Chengxi Ye, Yezhou Yang, Cornelia Fermuller +1
We explain that the difficulties of training deep neural networks come from a syndrome of three consistency issues. This paper describes our efforts in their analysis and treatment…
LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning
Chengxi Ye, Chen Zhao, Yezhou Yang +2
LightNet is a lightweight, versatile and purely Matlab-based deep learning framework. The idea underlying its design is to provide an easy-to-understand, easy-to-use and efficient…