EP-IT Data science seminars

Automatic Differentiation and Deep Learning

by Soumith Chintala (Facebook)

Europe/Zurich
222-R-001 (CERN)

222-R-001

CERN

200
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Description

Statistical learning has been getting more and more interest from the particle-physics community in recent times, with neural networks and gradient-based optimization being a focus.

In this talk we shall discuss three things:

  • automatic differention tools: tools to quickly build DAGs of computation that are fully differentiable. We shall focus on one such tool "PyTorch".
  •  Easy deployment of trained neural networks into large systems with many constraints: for example, deploying a model at the reconstruction phase where the neural network has to be integrated into CERN's bulk data-processing C++-only environment
  • Some recent models in deep learning for segmentation and generation that might be useful for particle physics problems.
Organized by

M. Girone, M. Elsing, L. Moneta, M. Pierini.......... Refreshments will be served at 13h30

Webcast
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