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PRODID:-//pretalx//pretalx.com//juliacon2021//speaker//LSJRYM
BEGIN:VEVENT
UID:pretalx-juliacon2021-EVR3HZ@pretalx.com
DTSTART:20210729T163000Z
DTEND:20210729T170000Z
DESCRIPTION:We present InvertibleNetworks.jl\, an open-source package for i
 nvertible neural networks and normalizing flows using memory-efficient bac
 kpropagation. InvertibleNetworks.jl uses manually implement gradients to t
 ake advantage of the invertibility of building blocks\, which allows for s
 caling to large-scale problem sizes. We present the architecture and featu
 res of the library and demonstrate its application to a variety of problem
 s ranging from loop unrolling to uncertainty quantification.
DTSTAMP:20260713T185701Z
LOCATION:Green
SUMMARY:InvertibleNetworks.jl - Memory efficient deep learning in Julia - P
 hilipp A. Witte\, Mathias Louboutin\, Ali Siahkoohi\, Felix J. Herrmann\, 
 Gabrio Rizzuti\, Bas Peters
URL:https://pretalx.com/juliacon2021/talk/EVR3HZ/
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BEGIN:VEVENT
UID:pretalx-juliacon2021-TXAWKU@pretalx.com
DTSTART:20210730T200000Z
DTEND:20210730T203000Z
DESCRIPTION:We present Redwood\, a Julia framework for clusterless supercom
 puting in the cloud. Redwood provides a set of distributed programming mac
 ros that enable users to remotely execute Julia functions in parallel thro
 ugh cloud services for batch and serverless computing. We present the arch
 itecture and design of Redwood\, as well as its application to existing Ju
 lia packages for machine learning and inverse problems.
DTSTAMP:20260713T185701Z
LOCATION:Red
SUMMARY:Redwood: A framework for clusterless supercomputing in the cloud - 
 Philipp A. Witte
URL:https://pretalx.com/juliacon2021/talk/TXAWKU/
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