18 citations · 32 across the 5 of their papers we have counts for
3 papers · 1 filter
Implicit Quantile Networks For Emulation in Jet Physics
B. Kronheim, A. Al Kadhim, M. P. Kuchera +2
The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task i…
Implicit Quantile Neural Networks for Jet Simulation and Correction
Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper +1
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (…
TensorBNN: Bayesian Inference for Neural Networks using Tensorflow
Braden Kronheim, Michelle Kuchera, Harrison Prosper
TensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models. The posterior density of neural network model parameters is repr…