Over the years, Machine Learning (ML) techniques have gained increasing attention from the large public. We believe that advances in ML will have a profound impact on our society: such impacts could be on productivity, employment, and competitiveness of companies or even nations. As ACML 2020 was planned to be held in Bangkok, Thailand, before going virtual, we would like to survey the state of ML applications and research in Thailand and neighbouring countries in South East Asia.
The workshop on Machine Learning in Thailand (MLIT) aims to bring together a diverse group of ML researchers, practitioners as well as researchers from other disciplines to present and discuss their projects with the other participants. Despite the online format, we hope that the workshop would help establish and stimulate ML research activities in Thailand and in the region.
The MLIT 2020 will be a virtual event where everyone is welcome to participate. There is no registration or participation fee. Below are links for participating the workshop:
|18:40-19:00||Virtual assistant and avatar: The first step|
AbstractThis talk presents the challenge of building a virtual assistant and avatar, focusing solely on speech-related issues. Then, we will share the idea of constructing an audiovisual speech dataset from found data (the video data that are available on the web). Finally, we will demonstrate the current state of our avatar.
|19:00-19:10||Transferable Reinforcement Learning for Board Games|
Contributed Talk 1
|19:10-19:20||Improving Google Colaboratory to serve Thailand Machine Learning Community|
Contributed Talk 2[Extended Abstract]
|19:20-19:30||High resolution weakly supervised localization architectures for medical images|
Contributed Talk 3
|19:30-19:40||Demystifying Machine Learning Algorithms with Methods of Theoretical Physics|
Contributed Talk 4[Extended Abstract]
|19:40-19:50||Break (10 minutes)|
|19:50-20:00||Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection|
Contributed Talk 5[Extended Abstract]
|20:00-20:10||Rapid Prototyping of an Inexpensive Camera with Low-Code Machine Learning Wildlife Recognition for Pangolin Conservation Research in Thailand|
Contributed Talk 6[Extended Abstract]
|20:10-20:20||A Large-Scale Data Collection from Internet for Thai Language and Speech Processing|
Contributed Talk 7[Extended Abstract]
|20:20-20:30||Thai-to-Any-Language Parallel Corpora from Wikipedia Dumps with CRF-based Sentence Segmentation and Multilingual Sentence Encoder|
Contributed Talk 8[Extended Abstract]
|20:30-20:40||End-to-end ML Pipelines in Big Retail: Showcases of Recommendation & Search Systems at TOPS Online|
Contributed Talk 9[Extended Abstract]