Learning to Learn

Add to your list(s) Download to your calendar using vCal

• Siddharth Swaroop; Will Tebbutt
• Thursday 23 November 2017, 13:30-15:00
• Engineering Department, CBL Seminar Room 4-38.

If you have a question about this talk, please contact Alessandro Davide Ialongo.

Abstract

Learning to Learn methods, or Meta-Learning, involve replacing hand-crafted aspects of conventional learning algorithms with more flexible features that can be learnt from data. We introduce and review recent work on learning to learn in the contexts of optimisation and few-shot learning.

Recommended Reading

There is no particular recommended reading, but the following papers will be discussed among others:

• “Learning to learn without gradient descent by gradient descent”, Chen et al., ICML 2017
• “Matching Networks for One Shot Learning”, Vinyals et al., NIPS 2016

This talk is part of the Machine Learning Reading Group @ CUED series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge University Engineering Department Talks
• Cambridge talks
• Centre for Smart Infrastructure & Construction
• Chris Davis' list
• Computational Continuum Mechanics Group Seminars
• Engineering Department, CBL Seminar Room 4-38
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Journal Clubs
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• ML
• Machine Learning Reading Group
• Machine Learning Reading Group @ CUED
• Machine Learning Summary
• Quantum Matter Journal Club
• Required lists for MLG
• School of Technology
• Simon Baker's List
• TQS Journal Clubs
• Trust & Technology Initiative - interesting events
• bld31
• custom
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.