Uc Berkeley Dissertation

Uc Berkeley Dissertation-77
This version was tested on pandoc version 1.12.4, however the version available in the ubuntu software center (1.12.3) should suffice.If it doesn’t you’ll have to manually install pandoc and create system links via which is a pandoc template to facilitation the conversion from to

Meta-learning algorithms “learn to learn” by using past data to learn how to adapt quickly to new tasks.

However, much of the initial work in meta-learning focused on designing increasingly complex neural network architectures.

Underlying our success are a strong tradition of collaboration, close ties to industry, and a supportive culture.

Explore our vibrant and dynamic community through this website or in person.

At a young age, Finn has become one of the most recognized experts in the field of robotic learning.

She has developed some of the most effective methods to teach robots skills to control and manipulate objects.

In her dissertation, Finn introduced a class of methods called model-agnostic meta-learning (MAML) methods, which don’t require computer scientists to manually design complex architectures.

Finn’s MAML methods have had tremendous impact on the field and have been widely adopted in reinforcement learning, computer vision and other fields of machine learning.

In the fall of 2019, she will start a full-time appointment as an Assistant Professor at Stanford University.

Finn received her Ph D in Electrical Engineering and Computer Science from the University of California, Berkeley and a BS in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.


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