Machine Learning and Likelihood Free Inference in Particle Physics (figshare.com) 47 points by aaronjg 9y ago ↗ HN
[–] darawk 9y 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 9y 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/paper...Here's an alternative technique for likelihood free inference: https://arxiv.org/abs/1506.02169 and a more recent approach http://beta.briefideas.org/ideas/5c2f74aedbf3618ca180382e393...making machine learning more robust to systematic uncertainties https://arxiv.org/abs/1611.01046A tech report summarizing Goodfellow's NIPS tutorial on GANs https://arxiv.org/abs/1701.00160 [–] jamessb 9y 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* slides 93 gives 3 references for using deep learning to classify jet images https://arxiv.org/abs/1511.05190 https://arxiv.org/abs/1603.09349 https://www.arxiv.org/abs/1609.00607* the reference for "Learning to Pivot with adversarial networks" is https://www.arxiv.org/abs/1611.01046
[–] cranmer 9y 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/paper...Here's an alternative technique for likelihood free inference: https://arxiv.org/abs/1506.02169 and a more recent approach http://beta.briefideas.org/ideas/5c2f74aedbf3618ca180382e393...making machine learning more robust to systematic uncertainties https://arxiv.org/abs/1611.01046A tech report summarizing Goodfellow's NIPS tutorial on GANs https://arxiv.org/abs/1701.00160
[–] jamessb 9y 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* slides 93 gives 3 references for using deep learning to classify jet images https://arxiv.org/abs/1511.05190 https://arxiv.org/abs/1603.09349 https://www.arxiv.org/abs/1609.00607* the reference for "Learning to Pivot with adversarial networks" is https://www.arxiv.org/abs/1611.01046
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[ 4.6 ms ] story [ 22.9 ms ] threadApproximate Bayesian Computation website does a good job of framing what is meant by likelihood free inference. https://approximatebayesiancomputational.wordpress.com/paper...
Here's an alternative technique for likelihood free inference: https://arxiv.org/abs/1506.02169 and a more recent approach http://beta.briefideas.org/ideas/5c2f74aedbf3618ca180382e393...
making machine learning more robust to systematic uncertainties https://arxiv.org/abs/1611.01046
A tech report summarizing Goodfellow's NIPS tutorial on GANs https://arxiv.org/abs/1701.00160
* slide 75 gives a reference for CARL: 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/1603.09349 https://www.arxiv.org/abs/1609.00607
* the reference for "Learning to Pivot with adversarial networks" is https://www.arxiv.org/abs/1611.01046