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Machine Learning Talks on Campus

Machine Learning Talks on Campus is an information service about talks, workshops and other events in the local community.

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If you want to announce a talk for the following week, please send an email to machine-learning@uni-heidelberg.de by Wednesday night.

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Current events

Tabelle

DateTitle/DescriptionHostVenue/Access
DateTitle/DescriptionHostVenue/Access
12.04.24
9:30 AM - 11 AM

Practical Equivariances via Relational Conditional Neural Processes
Dr. Manuel Haussmann, University of Southern Denmark

 

Conditional Neural Processes (CNPs) are a class of meta-learning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification. Many relevant machine learning tasks, such as in spatiotemporal modeling, Bayesian Optimization, and continuous control, inherently contain equivariances – for example to translation – which the model can exploit for maximal performance. However, prior attempts to include equivariances in CNPs do not scale effectively beyond two input dimensions.

In this talk, I will introduce the theory behind CNPs and discuss our recent proposal on how to incorporate equivariances into any neural process model and how we can ensure scalability to higher dimensions.

Fred Hamprecht
INF 205 Mathematikon
Seminarraum 10