A neural network trained with an extra checksum output can flag out-of-distribution predictions by measuring how strongly its own outputs violate the checksum relation.
Output-Constrained Bayesian Neural Networks
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abstract
Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates functional constraints about what the output can or cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN) represent an interpretable approach of enforcing a range of constraints, fully consistent with the Bayesian framework and amenable to black-box inference. We demonstrate how OC-BNNs improve model robustness and prevent the prediction of infeasible outputs in two real-world applications of healthcare and robotics.
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2024 1verdicts
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Soft Checksums to Flag Untrustworthy Machine Learning Surrogate Predictions and Application to Atomic Physics Simulations
A neural network trained with an extra checksum output can flag out-of-distribution predictions by measuring how strongly its own outputs violate the checksum relation.