Dosing models often use differential equations to model biological dynamics.

While extensive research on uncertainty estimation has been conducted with unimodal data, uncertainty estimation for multimodal data remains a challenge. Neural processes (NPs) have been demonstrated to be an effective uncertainty estimation method for unimodal data by providing the reliability of Gaussian processes with efficient and powerful DNNs.

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.

For out of distribution (OOD) inference, it is desired that the model can assign high epistemic uncertainty to the OOD regions compared to their ID counterparts. A.2 Policy Gradient based Reward Maximization for Segmentation Backbone This approach enables us to efficiently achieve the optimal solution for reward maximization. We present some examples of generated OOD examples in Figure 1(a). The results are presented in Figure 1(b)-(d). In Table 1, we present the results of our uncertainty estimation framework when applied to the Cityscapes dataset.

Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbalanced regression (VIR), which not only performs well in imbalanced regression but naturally produces reasonable uncertainty estimation as a byproduct. Different from typical variational autoencoders assuming I.I.D. representations (a data point's representation is not directly affected by other data points), our VIR borrows data with similar regression labels to compute the latent representation's vari-ational distribution; furthermore, different from deterministic regression models producing point estimates, VIR predicts the entire normal-inverse-gamma distributions and modulates the associated conjugate distributions to impose probabilistic reweighting on the imbalanced data, thereby providing better uncertainty estimation.