11 citations · 23 across the 6 of their papers we have counts for
6 papers
Accumulated Decoupled Learning: Mitigating Gradient Staleness in Inter-Layer Model Parallelization
Huiping Zhuang, Zhiping Lin, Kar-Ann Toh
Decoupled learning is a branch of model parallelism which parallelizes the training of a network by splitting it depth-wise into multiple modules. Techniques from decoupled learnin…
GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition
Wenyang Hu, Xiaocong Cai, Jun Hou +2
Connectionist Temporal Classification (CTC) and attention mechanism are two main approaches used in recent scene text recognition works. Compared with attention-based methods, CTC…
Fully Decoupled Neural Network Learning Using Delayed Gradients
Huiping Zhuang, Yi Wang, Qinglai Liu +2
Training neural networks with back-propagation (BP) requires a sequential passing of activations and gradients, which forces the network modules to work in a synchronous fashion. T…
Gradient-Free Learning Based on the Kernel and the Range Space
Kar-Ann Toh, Zhiping Lin, Zhengguo Li +2
In this article, we show that solving the system of linear equations by manipulating the kernel and the range space is equivalent to solving the problem of least squares error appr…
Deterministic Stretchy Regression
Kar-Ann Toh, Lei Sun, Zhiping Lin
An extension of the regularized least-squares in which the estimation parameters are stretchable is introduced and studied in this paper. The solution of this ridge regression with…
Constrained Maximum Correntropy Adaptive Filtering
Siyuan Peng, Badong Chen, Lei Sun +2
Constrained adaptive filtering algorithms inculding constrained least mean square (CLMS), constrained affine projection (CAP) and constrained recursive least squares (CRLS) have be…