Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management.

Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.

Wedesign a novel truncation operation to further reduce the computational complexity of theproposed algorithms.

A central question in computational neuroscience is how structure determines functioninneuralnetworks.

This application is often needed in safety-critical domains suchashealthcare, whereestimating andcommunicating uncertainty to decision-makers iscrucial. Weintroduce apractical approach for integrating uncertainty estimation into a class of state-of-the-art neural network methods used for individual-level causal estimates. We show that our methods enable us to deal gracefully with situations of "no-overlap", common in highdimensional data, where standard applications of causal effect approaches fail.