Suggested key words in the abstract (not inclusive):
• AI techniques and methods
• Generative models
• Pattern recognition
• Variational inference
• Optimisation techniques
• Sampling techniques
• Surrogate models
• Image analysis
• Time-series analysis
• Point-cloud analysis
• Symmetries and machine learning
• Physics-informed AI
• Diffusion models and related techniques
• Transformers and related techniques
• Foundation models
• Simulation-based/likelihood-free inference
• Likelihood-based and/or variational inference
• Integration of physics and ML
• Scientific machine learning
• Differentiable models and simulation codes
• Quantum ML
• Frugal AI
• Large Language Models
• AI for measurement/unfolding
• Other AI techniques
• AI usage
• AI for handling nuisance parameters
• Uncertainty quantification
• Explainable AI
• Trustworthy AI
• Frequentist vs Baysian inference in AI
• AI for discovery
• AI for experiment/accelerator control and operation
• AI for experimental design
• AI for simulation of physical systems
• AI for event reconstruction
• Other AI usage
• AI community
• Open software & Open data
• Benchmarks, datasets and models libraries
• ML training and platforms
• AI Ethics
• AI tools
• Community building
• Community consensus finding
• Computing, hardware and acceleration
• Sustainability of ML
• Hardware acceleration
• Sharing resources
• Neuromorphic computing
• FPGAs
• Use of HPCs
• AI in grid/cloud/distributed computing
• FastML (algorithms and tools)
