https://indico.cern.ch/event/689421/
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:
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automatic differention tools: tools to quickly build DAGs of computation that are fully differentiable. We shall focus on one such tool "PyTorch".
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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
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Some recent models in deep learning for segmentation and generation that might be useful for particle physics problems.
Please note that a Webcast retransmission will be available for this Seminar.