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Seminar Meta Learning (SemMeL)- Lecturers
- Prof. Dr.-Ing. habil. Andreas Maier, Dr. rer. nat. Patrick Krauß, Prof. Dr.-Ing. Joachim Hornegger
- Details
- Seminar
Online
2 cred.h, ECTS studies, ECTS credits: 5
nur Fachstudium, Sprache Englisch, This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again. Information regarding the online teaching will be provided in the studon course.
Zeit: Mon 8:15 - 9:45, KH 1.021
- Fields of study
- WPF INF-MA ab 1
WPF MT-MA-BDV ab 1
WPF CE-MA-TA-MT ab 1
- Prerequisites / Organisational information
- Registration via StudOn:
https://www.studon.fau.de/crs3330572.html
- Contents
- Meta-learning refers to algorithms which aim to learn an aspect of a learning algorithm from data.
Examples of meta-learning methods include algorithms which design neural network architectures based on data, optimize the performance of a learning algorithm or exploit commonalities between tasks to enable learning from few samples on unseen tasks.
These methods hold the promise to automate machine learning even further than learning good representations from data by learning algorithms to learn even better representations.The seminar will cover the most important works which provide the cornerstone knowledge to understand cutting edge research in the field of meta-learning. Applications will include:
Learning from few samples
Automatically tuning neural network architectures
Determining appropriate equivariances
Disentangling causal mechanisms
- Recommended literature
- Finn et al., "Model-agnostic meta-learning for fast adaptation of deep networks", ICML 2017
Zhou et al., "Meta-learning symmetries by reparameterization", Arxiv
Snell et al., "Prototypical networks for few-shot learning", Neurips 2017
Triantafillou et al., "Meta-dataset: A dataset of datasets for learning to learn from few examples", ICLR 2020
Vinyals et al., "Matching networks for one shot learning. ", Neurips 2016
Zoph et al. "Neural Architecture Search with Reinforcement Learning", Journal of Machine Learning Research 20 (2019)
Bengio et al., "A meta-transfer objective for learning to disentangle causal mechanisms", ICLR 2020
Santoro et al., "Meta-Learning with Memory-Augmented Neural Networks", ICML 2016
Ravi et al., "Optimization as a model for few-shot learning", ICLR 2016
Munkhdalai et al., "Meta Networks", ICML 2017
Sung et al. "Learning to Compare: Relation Network for Few-Shot Learning", CVPR 2018
Nichol et al. "On First-Order Meta-Learning Algorithms", Arxiv
- ECTS information:
- Credits: 5
- Additional information
- Keywords: algorithms; medical image processing
Expected participants: 10, Maximale Teilnehmerzahl: 16
Registration is required for this lecture.
Die Registration via: StudOn
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2020/2021:
- Seminar Meta Learning (SemMeL)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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