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12:15
|
--- Lunch break ---
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14:00
|
Invited speakers
(until 15:15)
()
|
|
14:00
|
Geometric Algebra Transformers: A Universal Architecture of Geometric Data
-
Taco Cohen
()
|
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15:15
|
--- Coffee break ---
|
|
16:00
|
Young Scientist Forum
(until 18:00)
()
|
|
16:00
|
End-To-End Latent Variational Diffusion Models for Unfolding LHC Events.
-
Alexander Shmakov
(University of California Irvine (US))
()
|
|
16:10
|
PC-Droid: Jet generation with diffusion
- Mr
Matthew Leigh
(University of Geneva)
()
|
|
16:20
|
Drapes: Diffusion for weak supervision
-
Debajyoti Sengupta
(Universite de Geneve (CH))
()
|
|
16:30
|
Self-supervised learning of jets using a realistic detector simulation
-
Nathalie Soybelman
(Weizmann Institute of Science (IL))
Etienne Dreyer
(Weizmann Institute of Science (IL))
Dmitrii Kobylianskii
(Weizmann Institute of Science (IL))
Nilotpal Kakati
(Weizmann Institute of Science (IL))
Patrick Rieck
(New York University (US))
()
|
|
16:40
|
De-noising Graph Super-Resolution with Diffusion Models and Transformers
-
Nilotpal Kakati
(Weizmann Institute of Science (IL))
()
|
|
16:50
|
Field-Level Inference with Microcanonical Langevin Monte Carlo
-
Adrian Bayer
(Princeton University / Simons Foundation)
()
|
|
17:00
|
Novel Approaches for Fast Simulation in HEP using Diffusion and Graph-to-Graph Translation
-
Dmitrii Kobylianskii
(Weizmann Institute of Science (IL))
()
|
|
17:10
|
Precision-Machine Learning for the Matrix Element Method
-
Theo Heimel
(Heidelberg University)
()
|
|
17:20
|
Galaxies and Graphs
-
Christian Kragh Jespersen
(Princeton University)
()
|
|
17:30
|
Simulation-based Self-supervised Learning (S3L)
-
Benedikt Maier
(KIT - Karlsruhe Institute of Technology (DE))
Jeffrey Krupa
(Massachusetts Institute of Technology)
()
|
|
17:40
|
Decorrelation using Optimal Transport
-
Malte Algren
(Universite de Geneve (CH))
()
|
|
17:50
|
The Interplay of Machine Learning–based Resonant Anomaly Detection Methods
-
Radha Mastandrea
(University of California, Berkeley)
()
|
|
18:30
|
Young Scientist Forum
(until 20:00)
()
|
|
18:35
|
Are Differentiable Simulators Beneficial for Cosmological Simulation-Based Inference?
-
Justine Zeghal
()
|
|
18:35
|
Cluster Scanning
- Mr
Ivan Oleksiyuk
(UNIGE)
()
|
|
18:35
|
Cosmic Perspectives: A Comparative Study of Image-to-Image Translation Methods with an Emphasis on Geometrical Alignment
-
Vitaliy Kinakh
(University of Geneva)
()
|
|
18:35
|
DeepTreeGAN: Fast Generation of High Dimensional Point Clouds
- Mr
Moritz Scham
(Deutsches Elektronen-Synchrotron (DE))
()
|
|
18:35
|
Diffusion-Based Separation of CMB and Dust Emission: Enabling Cosmological Inference
- Dr
Ruben Ohana
(Flatiron Institute (CCM))
Bruno Régaldo-Saint Blancard
(Flatiron Institute (CCM)) Mr
David Heurtel-Depeiges
(Flatiron Institute (CCM))
()
|
|
18:35
|
Domain adaption between SKA radio mocks and cosmological simulations
- Dr
Philipp Denzel
(Centre for Artificial Intelligence, ZHAW)
()
|
|
18:35
|
Finding strong lens by combining DenseLens and segmentation
-
Bharath Chowdhary Nagam
(Kapteyn Astronomical Institute)
()
|
|
18:35
|
Learning the Reionization History from High-z Quasar Damping Wings with Simulation-based Inference
-
Timo Kist
(Leiden Observatory)
()
|
|
18:35
|
Machine Learning based Compression of Scientific Data - the HEP Perspective
-
Pratik Jawahar
(University of Manchester (UK - ATLAS))
()
|
|
18:35
|
Pay Attention to Mean Fields for Point Cloud Generation
-
Benno Kach
(Deutsches Elektronen-Synchrotron (DE))
()
|
|
18:35
|
Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model
-
Mariia Drozdova
(Universite de Geneve (CH))
()
|
|
18:35
|
Sampling high-dimensional inverse problem posteriors with neural score estimation
- Mr
Benjamin Remy
(CEA Saclay)
()
|
|
18:35
|
The Calorimeter Pyramid: Rethinking the design of generative calorimeter shower models
-
Simon Schnake
(DESY / RWTH Aachen University)
()
|
|
18:35
|
Track finding and fitting with differentiable programming
-
Lucrezia Rambelli
(University of Genova (IT))
()
|
|
18:35
|
TURBO: The Swiss Knife of Auto-Encoders
-
Guillaume Quétant
(Université de Genève (CH))
()
|
|
18:35
|
Unlocking Autonomous Telescopes through Reinforcement Learning: An Offline Framework and Insights from a Case Study
-
Franco Terranova
(University of Pisa, Fermi National Accelerator Laboratory)
()
|
|
18:35
|
Unsupervised and Weakly Supervised Machine Learning Enhanced Anomaly Detection in High Energy Physics
-
Kinga Anna Wozniak
(Universite de Geneve (CH))
()
|
|
18:35
|
Using machine learning to detect antihydrogen in free fall
-
Lukas Golino
(Swansea University (GB))
()
|
|
20:00
|
Invited speakers
(until 21:15)
()
|
|
20:00
|
Physics-inspired learning on graphs
-
Michael Bronstein
()
|
