papers

Publications (6)

cs.LG2021

Error Controlled Actor-Critic

Xingen Gao, Fei Chao, Changle Zhou +5

On error of value function inevitably causes an overestimation phenomenon and has a negative impact on the convergence of the algorithms. To mitigate the negative effects of the ap…

cs.LG2019

Decoder Choice Network for Meta-Learning

Jialin Liu, Fei Chao, Longzhi Yang +2

Meta-learning has been widely used for implementing few-shot learning and fast model adaptation. One kind of meta-learning methods attempt to learn how to control the gradient desc…

cs.LG2019

Stock Prices Prediction using Deep Learning Models

Jialin Liu, Fei Chao, Yu-Chen Lin +1

Financial markets have a vital role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. In…

eess.SY2017

Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller

Yu Tsao, Hao-Chun Chu, Shih-Wei Lan +3

This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking sin-gle-layer CM…

cs.LG2019

Gradient Boost with Convolution Neural Network for Stock Forecast

Jialin Liu, Chih-Min Lin, Fei Chao

Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contain…

cs.CV2020

Task Augmentation by Rotating for Meta-Learning

Jialin Liu, Fei Chao, Chih-Min Lin

Data augmentation is one of the most effective approaches for improving the accuracy of modern machine learning models, and it is also indispensable to train a deep model for meta-…