This is the sixth annual workshop of the LPCC inter-experimental machine learning working group.
The workshop will be held on 29Jan-2Feb 2024 at CERN in a hybrid format, with remote participation made possible.
Confirmed invited speakers
- Jürgen Schmidhuber (IPSIA and KAUST): opening talk
- François Charton (META AI) on symbolic learning)
- Casey Fitzpatrick (Contextual AI), on LLMs
- Eliska Greplova (TU Delft), on quantum optics
- Gregor Kasieczka (UHamburg), on particle physics
- Jonas Kohler (Microsoft Research), on molecular physics
- Gaël Varoquaux (INRIA), on scientific inference
- Francesco Maria Velotti (CERN), on accelerators physics
- Gail Weiss (EPFL), full tutorial on transformers
If you receive any email by a "Global Travel Experts" company or any other similar company requesting your itinerary or other personal information or promising accommodation, please be aware it is a scam, and please report it to the CERN IT department https://information-technology.web.cern.ch/.
The structure of this year's workshop in terms of contributions is different from previous editions in so far it is focused on the poster presentations. The reason behind this approach is to be able to allocate a large number of contributions while promoting a strong interaction between the presenters/participants. For this reasons, we require the poster presenters to attend in person. A small number of contributed submissions will be selected for oral presentations.
You will have to arrange for your own accommodation, either in the CERN Hostel (https://edh.cern.ch/Hostel/, subject to room availability) or in nearby hotels.
Please make sure to be registered to lhc-machinelearning-wg@cern.ch CERN egroup, to be informed of any unforeseen circumstance.
The preliminary structure of the workshop includes:
- Tutorials
- Plenary invited talks from academy
- Plenary invited talks from industry
- Poster sessions
- Plenary contributed talks
For the contributed posters and potential talks, the following Tracks have been defined:
- ML for object identification and reconstruction
- ML for analysis : event classification, statistical analysis and inference, including anomaly detection
- ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model
- Fast ML : Application of Machine Learning to DAQ/Trigger/Real Time Analysis
- ML infrastructure : Hardware and software for Machine Learning
- ML training, courses, tutorial, open datasets and challenges
- ML for astroparticle
- ML for phenomenology and theory
- ML for particle accelerators
- Other
This workshop is organized by the CERN IML coordinators. To keep up to date with ML at LHC, please register to lhc-machinelearning-wg@cern.ch CERN egroup.
