Morse Neural Networks for Uncertainty Quantification

As a result, the development network, which generalizes the unnormalized of methods to quantify neural network uncertainty is an Gaussian densities to have modes of highdimensional increasingly important subject in deep learning research submanifolds instead of just discrete (Amodei et al., 2016). In particular, neural networks tend to points. Fitting the Morse neural network via a KLdivergence produce confidently wrong predictions when presented with loss yields 1) a (unnormalized) generative Out-Of-Distribution (OOD) inputs, that is, inputs that are density, 2) an OOD detector, 3) a calibration far away from the data distribution with which the model temperature, 4) a generative sampler, along was trained (Murphy, 2023; Nagarajan et al., 2021; Liu with in the supervised case 5) a distance awareclassifier.