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Machine Learning and Likelihood Free Inference in Particle Physics

• darawk 10y ago
  Anyone have a link to an actual paper on this? It seems interesting but this slideshow format is kind of hard to follow for me.
  • cranmer 10y ago
    Lots of topics and links throughout. Approximate Bayesian Computation website does a good job of framing what is meant by likelihood free inference. https://approximatebayesiancomputational.wordpress.com/papers-2/ https://approximatebayesiancomputational.wordpress.com/paper... Here's an alternative technique for likelihood free inference: https://arxiv.org/abs/1506.02169 https://arxiv.org/abs/1506.02169 and a more recent approach http://beta.briefideas.org/ideas/5c2f74aedbf3618ca180382e393c7617 http://beta.briefideas.org/ideas/5c2f74aedbf3618ca180382e393... making machine learning more robust to systematic uncertainties https://arxiv.org/abs/1611.01046 https://arxiv.org/abs/1611.01046 A tech report summarizing Goodfellow's NIPS tutorial on GANs https://arxiv.org/abs/1701.00160 https://arxiv.org/abs/1701.00160
  • jamessb 10y ago
    It's an Keynote/invited talk, so there isn't a single corresponding paper as such. There are reference to papers on some of the slides: * slide 75 gives a reference for CARL: https://arxiv.org/abs/1506.02169 https://arxiv.org/abs/1506.02169 * slides 93 gives 3 references for using deep learning to classify jet images https://arxiv.org/abs/1511.05190 https://arxiv.org/abs/1511.05190 https://arxiv.org/abs/1603.09349 https://arxiv.org/abs/1603.09349 https://www.arxiv.org/abs/1609.00607 https://www.arxiv.org/abs/1609.00607 * the reference for "Learning to Pivot with adversarial networks" is https://www.arxiv.org/abs/1611.01046 https://www.arxiv.org/abs/1611.01046