February 6, 2019 15:58


Speaker: Prof. Seungjin Choi (Pohang University of Science and Technology, Korea)

Meta-Learning: Few-shot learning and Warm-starting

Deep learning has achieved great success in various tasks, when it is trained with large amount of labeled data. However, it is a challenging task for deep models to learning from a handful of labeled examples. Few-shot learning, the goal of which is to learn from only a few examples in each class label, has been a popular subject in machine learning and computer vision communities. Recently, various attempts, including meta-learning, have been made to tackle the problem of few-shot learning. In this talk, I introduce recent advances in meta-learning, the underlying idea of which is to leverage past experience to learn a prior over tasks, so that it can quickly adapt to a novel task. I begin with proving an overview of meta-learning, explaining a few existing work. Then I introduce my own recent work: (1) MT-nets on gradient-based meta-learning; (2) TAEML, task-specific ensemble of meta-learners; (3) learning to warm-start Bayesian optimization.

More Information

Date February 8, 2019 (Fri) 09:30 - 11:00
URL https://c5dc59ed978213830355fc8978.doorkeeper.jp/events/86973


〒103-0027 Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi,Chuo-ku, Tokyo