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Found 7 papers, 4 papers with code
Effective LHC measurements with matrix elements and machine learning
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled.
Likelihood-free inference with an improved cross-entropy estimator
We extend recent work (Brehmer, et.
Mining gold from implicit models to improve likelihood-free inference
Simulators often provide the best description of real-world phenomena.
Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module
During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks.
A Guide to Constraining Effective Field Theories with Machine Learning
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments.
Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field theories at the LHC.
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
This leads to a new machine learning-based approach to likelihood-free inference that is complementary to Approximate Bayesian Computation, and which does not require a prior on the model parameters.
