Jul 24, 2023
Sprecher:in · 0 Follower:innen
In humans' classroom learning, many effective study techniques (e.g., the Feynman technique, peer questioning, etc.) have been developed to improve learning outcomes. We are interested in investigating whether these techniques can inspire the development of ML training strategies to improve bi-level optimization (BLO) based methods. Towards this goal, we develop a general framework - ClassMeta - which consists of basic elements such as learners, interaction functions, learning stages, etc. These elements can be flexibly configured to create various training strategies, each emulating a study technique of humans. In case studies, we apply ClassMeta to create new training strategies, by emulating the Feynman technique and peer questioning - two broadly adopted techniques in humans' classroom learning. These training strategies are used for improving two BLO based applications - neural architecture search and data weighting. Experiments on various datasets demonstrate the effectiveness of our methods.In humans' classroom learning, many effective study techniques (e.g., the Feynman technique, peer questioning, etc.) have been developed to improve learning outcomes. We are interested in investigating whether these techniques can inspire the development of ML training strategies to improve bi-level optimization (BLO) based methods. Towards this goal, we develop a general framework - ClassMeta - which consists of basic elements such as learners, interaction functions, learning stages, etc. These…
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