Learning to Teach
By Yang Fan, Fei Tian, Tao Qin, Xiang-Yang Li, Tie-Yan Liu. 2018
17 citations recorded by the source corpus.
Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students. In the field of artificial intelligence, however, one has not fully explored the role of teaching, and pays most attention to machine \emph{learning}. In this paper, we argue that equal attention, if not more, should be paid to teaching, and furthermore, an optimization framework (instead of heuristics) should be used to obtain good teaching strategies. We call this approach “learning to teach”. In the approach, two intelligent agents interact with each other: a student model (which corresponds to the learner in traditional machine learning algorithms), and a teacher model (which determines the appropriate data, loss function, and hypothesis space to facilitate the training of the student model). The teacher model leverages the feedback from the student model to optimize its own teaching strategies by means of reinforcement learning, so as to achieve teacher-student co-evolution. To demonstrate the practical value of our proposed approach, we take the training of deep neural networks (DNN) as an example, and show that by using the learning to teach techniques, we are able to use much less training data and fewer iterations to achieve almost the same accuracy for different kinds of DNN models (e.g., multi-layer perceptron, convolutional neural networks and recurrent neural networks) under various machine learning tasks (e.g., image classification and text understanding).
Subfields: Education
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Citations in the corpus
- Learning To Simulate (2018, background): “In contrast to Fan et al. (2018), who propose a similar setup, we focus on the actual data generation process q(x,y;ψ) and are not limited to selecting subsets of existing data.”
- Learning Hierarchical Teaching Policies for Cooperative Agents (2019, background): “rdness of tasks and design a curriculum for a learning agent to follow. Curriculum learning has many applications, including recent works that learn a training data curriculum for an image classifier [10, 16]. Another work of Thomaz and Breazeal [35] studies transferring human knowledge to an RL age”
- A Competence-aware Curriculum for Visual Concepts Learning via Question Answering (2020, background): “the diculty of each question. 2.2 Curriculum Learning and Machine Teaching The competence-aware curriculum in our work is related to curriculum learning [7,54,56,20,51,45,21,48] and machine teaching [61,62,39,11,41,15,58]. Curriculum learning is rstly proposed by Bengio et al. [7] and demonstrates t”