Year: 2020

By Liang Senwei, Department of Mathematics, Faculty of Science, on 20 January 2020

Machine learning has become an important area of research in scientific computing. In particular, deep neural networks with powerful universal approximation and generalisation capacity get more and more attention and they are applied in many applications, such as image classification, natural language processing. I am currently engaged in deep learning on image classification and on mathematics problems. This article mainly introduces my works on improving the generalisation of deep networks for image classification.

By Wang Junhong, Research Computing, NUS Information Technology, on 20 January 2020

Artificial Intelligence (AI) and deep learning became one of the hottest topics not only in the industrial and real life application development but also in the research domain. In NUS, researchers from different departments are working on research projects such as stereo matching, quantum many-body systems, computer vision, image classification, natural language processing and others by applying the AI and deep learning methodology. In this article, it is our pleasure to have three very active researchers sharing their on-going deep learning research works. Every researcher is also welcomed to share your research work and experience in using HPC/GPU resources.

By M Ganeshkumar, PhD Student, NUS Graduate School of Science and Engineering (NGS), Department of Electrical & Computer Engineering (ECE), on 20 January 2020

GPUs/TPUs are used to increase the processing speed when training deep learning models due to its parallel processing capability. Reinforcement learning on the other hand is predominantly CPU intensive due to the sequential interaction between the agent and environment. With the recent popularity of deep reinforcement learning (deep RL) algorithms, understanding how to shorten processing speed based on the available resources becomes imperative. The purpose of this report is to highlight some considerations to optimise the computational resources available for deep RL, specifically the size of network used.