Wednesday, December 4, 2024. 

Time: 16:10-17:00

venue: Mathematics Building Room 527

Speaker: Dr. Chiu, Sheng-Min (Raydium Corp.)

Abstract:

Deep learning models (DLMs) have gained widespread application in various domains in recent years. Researchers typically provide rich datasets to enable DLMs to better learn target tasks. However, the high computational cost of DLMs remains a significant barrier to their widespread adoption. This thesis proposes a research study that addresses this challenge by introducing interpretable neurons for feature selection and cost reduction in DLMs. This study focuses on two distinct domains: industrial applications and spatio-temporal databases. In the domain of industrial applications, a lightweight framework for predicting tool wear is proposed. In the spatio-temporal database domain, research is conducted on various aspects, including feature selection and cost reduction for real estate price prediction and crowd flow prediction. Each of these domains is extensively discussed in dedicated chapters throughout the thesis. All proposed methods are thoroughly examined, simulated, analyzed, and validated through experiments. The experimental
results demonstrate the efficacy of the proposed approaches in addressing the high computational costs associated with DLMs