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• W2892359699 abstract "Teaching is critical to human society: it is with teaching that prospective students are educated and human civilization can be inherited and advanced. A good teacher not only provides his/her students with qualified teaching materials (e.g., textbooks), but also sets up appropriate objectives (e.g., course projects and exams) considering different situations of a student. When it comes to artificial intelligence, treating machine models as students, the loss that are optimized act as perfect counterparts of the objective set by the teacher. In this work, we explore the possibility of imitating human teaching behaviors by dynamically and automatically outputting appropriate loss to train machine models. Different from typical settings in which the loss function of a machine model is predefined and fixed, in our framework, the loss function of a machine model (we call it student) is defined by another machine model (we call it teacher). The ultimate goal of teacher model is cultivating the student to have better performance measured on development dataset. Towards that end, similar to human teaching, the teacher, a parametric model, dynamically outputs different loss that will be used and optimized by its student model at different training stages. We develop an efficient method for the teacher model that makes gradient based optimization possible, exempt of the ineffective solutions such as policy optimization. We name our method as learning to teach with dynamic loss functions (L2T-DLF for short). Extensive experiments on real world tasks including image classification and neural machine translation demonstrate that our method significantly improves the quality of various student models." @default.
• W2892359699 created "2018-09-27" @default.
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• W2892359699 date "2018-10-29" @default.
• W2892359699 modified "2023-09-22" @default.
• W2892359699 title "Learning to Teach with Dynamic Loss Functions" @default.
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