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NEW TIME - 1 to 2 p.m. CSL SINE SEMINAR, Yuxiong Wang, Robotics Institute Carnegie Mellon

Event Type
Seminar/Symposium
Sponsor
Coordinated Science Lab
Location
B02 Coordinated Science Lab
Date
Dec 6, 2019   1:00 - 2:00 pm  
Speaker
Yuxiong Wang, Carnegie Mellon University
Contact
Peggy Wells
Phone
217-244-2646
Views
276
Originating Calendar
CSL SINE Group

Title: Learning to Learn More with Less

Abstract:
Understanding how humans and machines learn from few examples remains a fundamental challenge. Humans are remarkably able to grasp a new concept from just few examples, or learn a new skill from just few trials. By contrast, state-of-the-art machine learning techniques typically require thousands of training examples and often break down if the training sample set is too small. In this talk, I will discuss our efforts towards endowing visual learning systems with few-shot learning ability. Our key insight is that the visual world is well structured and highly predictable not only in feature spaces but also in under-explored model and data spaces. Such structures and regularities enable the systems to learn how to learn new tasks rapidly by reusing previous experiences. I will focus on a few topics to demonstrate how to leverage this idea of learning to learn, or meta-learning, to address a broad range of few-shot learning tasks: meta-learning in model space and task-oriented generative modeling. I will also discuss some ongoing work towards building machines that are able to operate in highly dynamic and open environments, making intelligent and independent decisions based on insufficient information.

Bio: Yuxiong Wang is a postdoctoral fellow in the Robotics Institute at Carnegie Mellon University. He received a Ph.D. in robotics in 2018 from Carnegie Mellon University. His research interests lie in the intersection of computer vision, machine learning, and robotics, with a particular focus on few-shot learning and meta-learning. He has spent time at Facebook AI Research (FAIR).

link for robots only