no code implementations • 15 Jan 2022 • YuAn Wang, Chunyuan Zhang, Tianzong Yu, Meng Ma
RLSNA2C uses the Kronecker-factored approximation, the RLS method and the natural policy gradient to learn the compatible parameter and the policy parameter.
no code implementations • 13 Jan 2022 • Tianzong Yu, Chunyuan Zhang, YuAn Wang, Meng Ma, Qi Song
Convolutional neural networks (CNNs) have succeeded in many practical applications.
no code implementations • 13 Jan 2022 • Chunyuan Zhang, Chao Liu, Qi Song, Jie Zhao
However, limited by the strong correlation among sequential samples of the agent, ESN-based policy control algorithms are difficult to use the recursive least squares (RLS) algorithm to update the ESN's parameters.
no code implementations • 7 Sep 2021 • Chunyuan Zhang, Qi Song, Hui Zhou, Yigui Ou, Hongyao Deng, Laurence Tianruo Yang
In this paper, to overcome these drawbacks, we propose three novel RLS optimization algorithms for training feedforward neural networks, convolutional neural networks and recurrent neural networks (including long short-term memory networks), by using the error backpropagation and our average-approximation RLS method, together with the equivalent gradients of the linear least squares loss function with respect to the linear outputs of hidden layers.
no code implementations • 6 Mar 2019 • Deguang Wang, Junzhong Shen, Mei Wen, Chunyuan Zhang
Three-dimensional deconvolution is widely used in many computer vision applications.
Distributed, Parallel, and Cluster Computing
no code implementations • 7 Sep 2014 • Lei Luo, Chunhua Shen, Xinwang Liu, Chunyuan Zhang
We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition.