Conveners
Plenary Session Thursday
- Jim Pivarski (Princeton University)
- Eduardo Rodrigues (University of Liverpool (GB))
Classical minimization methods, like the steepest descent or quasi-Newton techniques, have been proved to struggle in dealing with optimization problems with a high-dimensional search space or subject to complex nonlinear constraints, in addition to requiring continuous cost functions. For these reasons, in the last decade, the interest on metaheuristic nature-inspired algorithms has been...
Experimental High Energy Physics has entered an era of precision measurements. However, measurements of many of the accessible processes assume that the final states' underlying kinematic distribution is the same as the Standard Model prediction. This assumption introduces an implicit model-dependency into the measurement, rendering the reinterpretation of the experimental analysis complicated...
The increasing application of Machine Learning (ML) in High Energy Physics (HEP) analysis and reconstruction necessitates the use of GPUs. The extensive runtimes associated with training neural networks make them vulnerable to runtime constraints and failures.
Checkpointing, which involves storing the current state of the training persistently, offers a solution to these challenges. It...
