50 citations · 284 across the 21 of their papers we have counts for
5 papers · 1 filter
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…
MADLens, a python package for fast and differentiable non-Gaussian lensing simulations
Vanessa Böhm, Yu Feng, Max E. Lee +1
We present MADLens a python package for producing non-Gaussian lensing convergence maps at arbitrary source redshifts with unprecedented precision. MADLens is designed to achieve h…
Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
Biwei Dai, Uros Seljak
The goal of generative models is to learn the intricate relations between the data to create new simulated data, but current approaches fail in very high dimensions. When the true…
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…
Compact Radio Emission from Nearby Galaxies with Mid-infrared Nuclear Outbursts
B. B. Dai, X. W. Shu, N. Jiang +5
We present 5.5 GHz observations with the VLA of a sample of nearby galaxies with energetic nuclear outbursts at mid-infrared (MIR) bands. These observations reach a uniform depth d…