We also nuance the idea that learning happens at the edge of chaos by giving evidence that avery desirable feature forneural networks isthehyperbolicity of their Jacobian at initialization.

Ensembles of geophysical models improve prediction accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotemporally varying model weights and bias, while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertaintyaware predictions without sacrificing interpretability.

To mitigate the threat, existing solutions attempted to search for backdoor triggers, which can be time-consuming when handling a large search space.

We contributeNeuralSolver, a novel recurrent solver that can efficiently and consistently extrapolate, i.e., learn algorithms from smaller problems (in terms of observation size) and execute those algorithms in large problems.