「EXPLAINABLE LIFELONG STREAM LEARNING」
CcS-L5G-serBOTinQ セミナー & 技術討論

【 事前参加申し込み / Registration 】
　こちらのURLより、事前参加申し込みをお願いいたします。
　https://forms.gle/ofQDt37Cb18sdFnb8

【 日時 / Date and Time 】
　2023年8月7日(月) 13:00-16:00
　August 7, 2023 (Mon) 1:00 – 4:00 p.m.

【 場所 / Venue 】
　東京都立大学 日野キャンパス1号館会議室2-3
　（住所：〒191-0065　東京都日野市旭が丘6-6），ハイブリッド
　Tokyo Metropolitan University, Hino Campus, Bldg. 1, Conference Room 2-3
　 (Address :  6-6 Asahigaoka, Hino, Tokyo, 191-0065), Hybrid

【 講師 / Lecturer 】
　Prof. Loo Chu Kiong

【 所属 / Affiliation 】
　University of Malaya

【 概要 / Outline 】
　Real-time on-device continual learning applications are used on mobile phones, consumer robots, and smart appliances. Such devices have limited processing and memory storage capabilities, whereas continual learning acquires data over a long period of time. By necessity, lifelong learning algorithms have to be able to operate under such constraints while delivering good performance. This study presents the Explainable Lifelong Learning (ExLL) model, which incorporates several important traits: 1) learning to learn, in a single pass, from streaming data with scarce examples and resources; 2) a self-organizing prototype-based architecture that expands as needed and clusters streaming data into separable groups by similarity and preserves data against catastrophic forgetting; 3) an interpretable architecture to convert the clusters into explainable IF-THEN rules as well as to justify model predictions in terms of what is similar and dissimilar to the inference; and 4) inferences at the global and local level using a pairwise decision fusion process to enhance the accuracy of the inference.