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cs.LG2020★ 22 cited
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
George Stein, Uros Seljak, Biwei Dai
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data. Here we in…
cs.LG2020★ 4 cited
Sliced Iterative Normalizing Flows
Biwei Dai, Uros Seljak
We develop an iterative (greedy) deep learning (DL) algorithm which is able to transform an arbitrary probability distribution function (PDF) into the target PDF. The model is base…