Introduction to Differentiable Programming In Hep

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Differentiable Programming In Hep Comprehensive Overview

Lukas Heinrich, TU Munich. This tutorial will cover how to optimise various aspects of analyses -- such as cuts, binning, and learned observables like neural ... In this insightful talk, Valentin Churavy (University of Augsburg) explores

In the ideal world, we describe our models with recognizable mathematical expressions and directly fit those models to large data ...

Summary & Highlights for Differentiable Programming In Hep

  • Differentiable Programming
  • Deep learning has led to encouraging successes in many challenging tasks. However, a deep neural model lacks interpretability ...
  • e-Seminar on Scientific Machine Learning Speaker: Dr. Jan Drgona (PNNL) Abstract: In this talk, we will present a
  • In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course.
  • by Lukas Heinrich.

In summary, understanding Differentiable Programming In Hep gives us a better perspective.

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