Research Highlight

 Invitation to attend a special talk

 Title: “Explainable Lifelong Streaming Learning”

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 could 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, hence “Glocal Pairwise Fusion.” We compare ExLL against contemporary online learning algorithms for image recognition, using OpenLoris, F-SIOL-310, and Places datasets to evaluate several continual learning scenarios for video streams, low-sample learning, ability to scale, and imbalanced data streams. The algorithms are evaluated for their performance in accuracy, number of parameters, and experiment runtime requirements. ExLL outperforms all algorithms for accuracy in most of the tested scenarios.

Instructor: Professor Dr. Chu Kiong Loo, Department of Artificial Intelligence, Faculty of Computer Science & Information Technology, Universiti Malaya.

Date: 12 March 2024
Time: 10:30AM – 12:00PM
Place: Room M23, 1st Flr. School of Information Technology, KMITL

Register click here
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Contact: rattana@localhost

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