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The First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward
Huaxiu Yao · Hugo Larochelle · Percy Liang · Colin Raffel · Jian Tang · Ying WEI · Saining Xie · Eric Xing · Chelsea Finn

Sat Jul 23 05:50 AM -- 02:30 PM (PDT) @ Hall F
Event URL: https://pretraining.github.io/ »

The past five years have seen rapid progress in large-scale pre-trained models across a variety of domains, such as computer vision, natural language processing, robotics, bioinformatics, etc. Leveraging a huge number of parameters, large-scale pre-trained models are capable of encoding rich knowledge from labeled and/or unlabeled examples. Supervised and self-supervised pre-training have been the two most representative paradigms, through which pre-trained models have demonstrated large benefits on a wide spectrum of downstream tasks. There are also other pre-training paradigms, e.g., meta-learning for few-shot learning, where pre-trained models are trained so that they quickly adapt to solve new tasks. However, there are still many remaining challenges and new opportunities ahead for pre-training, In this workshop, we propose to have the following two foci: (1) Which pre-training methods transfer across different applications/domains, which ones don't, and why? (2) In what settings should we expect pre-training to be effective, compared to learning from scratch?

Author Information

Huaxiu Yao (Stanford University)
Hugo Larochelle (Google Brain)
Percy Liang (Stanford University)
Colin Raffel (Google Brain)
Jian Tang (Mila)
Ying WEI (City University of Hong Kong)
Saining Xie (Facebook)
Eric Xing (Petuum Inc. and CMU)
Chelsea Finn (Stanford)

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for learning reward functions underlying behavior, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.

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