Debating the potential of machine learning in astronomical surveys

Debates

Debate 1 (Monday): What flavours of machine learning techniques are most appropriate for astronomy?

Recording: https://youtu.be/FcmU3PGssek

Chair: Tom Charnock

Marc Huertas-Company
François Lanusse
Anastase Charantonis
Laurence Perreault-Levasseur

 

Debate 2 (Tuesday): Can machine learning models be considered on the same footing as physical models?

Recording: https://youtu.be/g2N4tH3BruM

Chair: Guihem Lavaux

Tom Charnock
Torsten Ensslin
Alan Heavens
Hiranya Peiris

 

Debate 3 (Wednesday): How do we understand what the machine has understood?

Recording: https://youtu.be/aQX2HPvkINQ

Chair: Torsten Enßlin

Rémi Flamary
Shirley Ho
Bhuvnesh Jain
Ben Wandelt

 

Debate 4 (Thursday): What would it take for the community to accept the findings?

Recording: https://youtu.be/hQBj9DdNavc

Chair: H.J. Mc Cracken

Licia Verde
David Hogg
Alexandre Refregier
Frederic Courbin

 

Debate 5 (Friday): "What can machine learning not do ... yet ?"

Recording: https://youtu.be/lftxxKkETrU

Chair: Ben Wandelt

Romain Teyssier
Brice Ménard
Chiara Ferrari
Jens Jasche

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