5 citations · 10 across the 4 of their papers we have counts for
4 papers · 1 filter
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Lucas Kook, Chris Kolb, Philipp Schiele +8
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithm…
Probabilistic Time Series Forecasts with Autoregressive Transformation Models
David Rügamer, Philipp F. M. Baumann, Thomas Kneib +1
Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure…
Translational Equivariance in Kernelizable Attention
Max Horn, Kumar Shridhar, Elrich Groenewald +1
While Transformer architectures have show remarkable success, they are bound to the computation of all pairwise interactions of input element and thus suffer from limited scalabili…
Deep Conditional Transformation Models
Philipp F. M. Baumann, Torsten Hothorn, David Rügamer
Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditiona…