Name: Conceptual Advances in Deep Learning for Research on Universe and Matter
Start: 2022-09-12T18:00:00+02:00
End: 2022-09-16T14:00:00+02:00
Location: Hotel zur Post

Monday, September 12
- 6:00 PM
  
  Supper
- Reinforcement Learning
  - 1
    
    Introduction
    
    welcome_&_farewell_wiehl_workshop_v9.pdf
  - 2
    
    Keynote: Reinforcement Learning in the research field of accelerators
    
    Speaker: Dr Andrea Santamaria Garcia (KIT)
    
    reinforcement-learning.pdf
    
    RL Maze Example
Tuesday, September 13
- Participant Contributions
  - 3
    
    Reinforced Jet-Parton Assignment for Particle Physics Analyses
    
    Speaker: Dennis Noll (RWTH Aachen University (DE))
    
    220913_noll-assignment.pdf
  - 4
    
    Open Neural Network Exchange Format - An Introduction
    
    Speaker: Dr Andreas Fehlner
    
    2022_09_13_OnnxTalk_Fehlner.pdf
  - 5
    
    CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks
    
    Speaker: Sven Günther
    
    ErUM_talk_Wiehl_Guenther.pdf
  - 6
    
    The State of the Art of Deep-Learning-Based Event Classification and Reconstruction for IACTs
    
    Speaker: Dr Samuel Spencer
    
    Erum Expert Workshop 2022- Spencer.pdf
- Transformer
  - 7
    
    Keynote: Transformer
    
    Speaker: Niklas Langner (RWTH Aachen University)
    
    Wiehl_Transformer.pdf
  - 8
    
    Introduction to hackathon
    
    Speaker: Niklas Langner (RWTH Aachen University)
    
    Hackthon_Transformer.pdf
- Transformer
  - 9
    
    Hackathon: Transformer
    
    Speaker: Niklas Langner (RWTH Aachen University)
- Transformer
  - 10
    
    Hackathon: Transformer
Wednesday, September 14
- Analytic Expressions
  - 11
    
    Key note: Transfer of neural network probability to analytic expressions
    
    Speaker: Tilman Plehn (University of Heidelberg)
    
    Symbolic Regression
- Graph Neural Networks
  - 12
    
    Introduction to hackathon on Graph Neural Networks
    
    Speaker: Niklas Langner (RWTH Aachen University)
    
    Hackathon_Graph.pdf
- Graph Neural Networks
  - 13
    
    Hackathon: Graph Neural Networks
    
    Speaker: Niklas Langner (RWTH Aachen University)
- Participant Contributions
  - 14
    
    Bayesian neural networks for particle identification
    
    Speaker: Prof. Johannes Erdmann
    
    WiehlWorkshop2022.pdf
  - 15
    
    Boosting mono-jet searches with model agnostic deep learning
    
    Speaker: Thorben Finke
    
    Boosting mono-jet searches with model agnostic machine learning-2.pdf
  - 16
    
    Super-resolution of photon calorimeter images using generative adversarial networks
    
    Speaker: Florian Mausolf
    
    Super-resolution_photons_ErUM.pdf
- Participant Contributions
  - 17
    
    An ML approach to the classification of phase transitions in many flavor QCD
    
    Speaker: Marius Neumann (Bielefeld University)
    
    Neumann_ErUM_Wiehl.pdf
  - 18
    
    A Machine Learning Approach to Searching Dark Matter Subhalos in Fermi-LAT Sources
    
    Speaker: Kathrin Nippel (RWTH Aachen University (TTK))
    
    DMSubhalos_KathrinNippel_ErUM_Workshop.pdf
  - 19
    
    Virtual Patho-Histology at the GINIX 3D X-ray Microscope
    
    Speaker: Dr Markus Osterhoff
    
    CAiDLfRoUaM_mo1.pdf
Thursday, September 15
- Participant Contributions
  - 20
    
    Deep Learning-based Imaging in Radio Interferometry
    
    Speaker: Stefan Fröse
    
    radio_deep_learning.pdf
  - 21
    
    Information Field Theory -- Bayesian Inference for Fields
    
    Speaker: Jakob Roth
    
    Jakob_Roth_IFT.pdf
  - 22
    
    Interpolating Calibration Data on the Sphere with Information Field Theory
    
    Speaker: Mr Maximilian Straub
    
    Wiehl Straub.pdf
- Normalizing Flows
  - 23
    
    Keynote: Applications and Potentials of Normalising Flows
    
    Speaker: Gregor Kasieczka (University Hamburg)
    
    DeepLearning_Wiehl_2022.pdf
  - 24
    
    Introduction to hackathon
    
    Speakers: Gregor Kasieczka (University Hamburg), Mr Maximilian Straub
    
    Flow Exercise
- Normalizing Flows
  - 25
    
    Hackathon: Normalizing flows, (conditional) invertible networks
    
    Speakers: Gregor Kasieczka (University Hamburg), Mr Maximilian Straub
- Normalizing Flows
  - 26
    
    Hackathon: Normalizing flows, (conditional) invertible networks
    
    Speakers: Gregor Kasieczka (University Hamburg), Mr Maximilian Straub
Friday, September 16
- Autoencoder
  
  Autoencoder_Wiehl.pdf
  - 27
    
    Key note: Autoencoder
    
    Speaker: Michael Krämer (RWTH Aachen University)
    
    Autoencoder_Wiehl.pdf
  - 28
    
    Introduction to hackathon
    
    Speakers: Michael Krämer (RWTH Aachen University), Thorben Finke
    
    Autoencoder Hackathon
- Autoencoder
  
  Autoencoder_Wiehl.pdf
  - 29
    
    Hackathon: Autoencoder, anomaly search, latent space
    
    Speakers: Michael Krämer (RWTH Aachen University), Thorben Finke
  - 30
    
    Farewell
    
    farewell_wiehl_workshop.pdf
- 1:00 PM
  
  Lunch