Big Data, Deep Learning, and huge computing are shaping up AI and are transforming our society. Especially, learning with deep neural networks has achieved great success across a wide variety of tasks. To help increase speed to solution and reduce duplication of effort, automated model construction is of great interest to provide architecture-effective and domain-adaptive deep models. On the other hand, understanding model behaviors and building trust in model prediction are of particular importance in many applications such as autonomous driving, medical and fintech tasks. The research of automated and interpretable deep learning should at least include the following key components: (1) neural architecture search (2) model construction with a changing environment such as transfer learning and (3) understanding and interpretation of deep learning models.
In this panel we plan to focus on timely topics of above areas. The panel will include a comprehensive survey of state-of-the-art algorithms and systems, a detailed description of the presenters’ research experience, and live-demonstration of platforms built by the Baidu AutoDL team. Through this panel, attendees will gain an understanding of how to efficiently build automated deep learning models and enhance their trustworthiness. Our panel will also speed up the process of turning deep learning research results into industrial products by introducing Baidu AutoDL, a tool that facilitates automated and interpretable deep learning.
Presenters: Bolei Zhou, Yi Yang, Quanshi Zhang, Dejing Dou, Haoyi Xiong, Jiahui Yu, Humphrey Shi, Linchao Zhu, Xingjian Li
Talk(Haoyi Xiong): 6:30-7:00 (Los Angeles) / 21:30-22:00 (Beijing), 12/07/2020
Panel: 7:00-7:40 (Los Angeles) / 22:00-22:40 (Beijing), 12/07/2020
Live Zoom Room: