The agenda is final. Slides and talk recordings are publicly available from the "Timetable"
The event is run in hybrid mode, however, connection details are only made available to registered participants. .
- Tue 19 - Wed 20 Apr : Workshop on Representation Learning from Heterogeneous/Graph-Structured Data
- Thu 21 Apr - Fri 22 Apr : Workshop on Dealing with Uncertainties
- Mon 25 - Tue 26 : Workshop on Generative Models
- Wed 27 - Fri 29th : Learning to Discover AI and Physics conference
Learning To Discover is a program on Artificial Intelligence and High Energy Physics (HEP) to take place at Institut Pascal Paris-Saclay 19th Apr 2022 to 29th Apr 2022, in its beautiful new building. Over the two weeks, three themes will be successively tackled during innovation-oriented sessions of two days each, followed by a three days general conference on AI and physics.
Although many participants will be on-site, Institut Pascal is fully equipped with technology allowing remote participation as seamless as possible (to registered participants only).
Although vaccination is not mandatory anymore, it it still strongly recommended. Masks wearing strongly recommended in the building. CO2 levels are monitored throughout the building.
Since 2014, the field of AI and HEP has grown exponentially. Physicists have realised quickly the potential of AI to deal with the large amount of complex data they are collecting and analysing. Many AI techniques have been put forward, with scientific collaboration based on open data sets, challenges, workshops and papers.
Learning To Discover is a first-of-its-kind program where participants will have access to deep technical insights in advanced machine learning techniques, and their application to particle physics.
Three themes have tentatively been selected based on the one hand, their interest for HEP, and the fact there is already a number of HEP teams working on it, on the other hand, their importance in the Machine Learning field.
Representation Learning over Heterogeneous/Graph Data workshop
HEP data are rarely image-like nor tabular. Despite promising applications of Graph Neural Network (GNN) to several HEP problems (tracking, classification, calorimeter reconstruction, particle flow, ...) there remains several bottlenecks towards getting models into production, in terms of both accuracy and inference speed. Multiple variants (Graph Attention Network, Transformers, ...) are available, with improved performance and resource requirements. The following topics are of interest: ways to extract information (at graph, node, or edge level), mechanisms for propagating information throughout the graph, generation of graph structure, evolution of graph topology within the model, etc...
Dealing with Uncertainties workshop
Physicists ultimately write papers with measurements which always include an assessment of uncertainties. How to evaluate the uncertainty on a trained model ? How to build confidence into a ML model and how to convince our peers about it ? How to deal with the uncertainties on the input of the models to maximise the overall accuracy ? Bayesian Networks, Adversarial architectures and other techniques are being explored.
Generative Models workshop
High Energy Physics is well known for having built accurate simulators which are able to deliver simulation of real experiments, from the Quantum Field Theory equations dealing with the interactions of fundamental particles up to specific answers of electronic circuits. These simulations are accurate, but not perfect, and very slow. Proof of Concept of using Generative Models (GAN, VAE,...) are being developed but many issues are to be solved before they make it to production. How to reach sufficient accuracy over a large parameter space ? How to make sure multi-dimension correlations are well described ? How to deal with the irregularity of the detector (think of an image with pixels of different sizes and shapes) ?
Physicists with concrete experience with Machine Learning (postdocs, advanced PhD students, ...) and Computer Scientists seasoned with the development of cutting edge ML models (expected participation from Google Deepmind, NVidia, IDIAP, ...) are invited to apply for attendance. Application is however open to everyone with experience and interest. The selection process will be as inclusive as possible, and only done so as to make the workshop fruitful for everyone and given the constraints of the premises. The final general conference on AI and HEP is open to physicists and Computer Scientists until the maximum attendance is reached.
Learning to Discover : AI and High Energy Physics conference
The final conference will cover cutting edge topics in the field with keynote talks, summaries of the preceding workshops, as well as talks from the call for contributions.
Learning To Discover is hosted at the new Université Paris-Saclay Institut Pascal, an ideal venue for such interactions to take place. Located in Orsay (close to Paris) in the heart of Université Paris-Saclay, it has offices for up to 60 people, meeting rooms, amphitheaters (up to 120 seats) and all amenities for a successful workshop. In the usual Institut Pascal format, lunches and social programme (one dinner per workshop or for the conference) is free. Accommodation can be booked in the area on Institut Pascal expense for the workshops participants; travel is covered only if needed. Registration to the workshops is free (but moderated) as well as registration to the final conference. See "Support and Accommodation" item in the menu bar for more practical details. Participation is limited to 40 on site for each of the workshop, and 120 on site for the conference.
A special issue of Computing and Software for the Big Science (a refereed journal) will be prepared with the most relevant contributions with a submission deadline 4 months after the workshop, leaving ample time for finalisation of the studies showcased at the workshop. A number of workshops concerning AI and Physics have been organised in recent years, notably “Deep Learning for the Physical Science” at NeurIPS 2017 (https://dl4physicalsciences.github.io/), “Machine Learning and the Physical Science” at Neurips 2019, 2020 and 2021 (https://ml4physicalsciences.github.io) and “AI and physics” at AMLD 2020 and 2021 in Lausanne (https://appliedmldays.org/tracks/ai-physics). Studies developed at “Learning To Discover” will have a high chance at being accepted at future such events.