How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression

arXiv:2405.05429v1 Announce Type: new
Abstract: Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms. However, neural representations of distributional regression models, such as the Cox model, have received little attention so far. We close this gap by proposing a framework for distributional regression using inverse flow transformations (DRIFT), which includes neural representations of the aforementioned models. We empirically demonstrate that the neural representations of models …

abstract algorithms arxiv attention cs.ai cs.lg deep learning deep learning algorithms however linear linear regression network neural network regression serve simple stat.co stat.ml type