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Found 3 papers, 2 papers with code
Exploiting Field Dependencies for Learning on Categorical Data
Instead of modelling statistics of features globally (i. e., by the covariance matrix of features), we learn a global field dependency matrix that captures dependencies between fields and then we refine the global field dependency matrix at the instance-wise level with different weights (so-called local dependency modelling) w. r. t.
Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires
We show that popular optimisation cost functions used in the literature can result in PINNs that fail to maintain temporal continuity in modelled fire-fronts when there are extreme changes in exogenous forcing variables such as wind direction.
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution
We propose a novel approach to perform approximate Bayesian inference in complex models such as Bayesian neural networks.
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