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Apple Silicon machines with large unified memory make it possible to hold large tensors on a desktop GPU, but PyTorch's Metal Performance Shaders backend silently returns wrong results for batched matrix multiplication with more than $2^{32}$ elements is found.
This work proposes observed-block multi-prediction, which restricts the multi-prediction objective to targets drawn from what each row actually observes, well defined for any missingness pattern and reduces to the original criterion when rows are complete.
It is argued that classical EBMs, exemplified by the DBM, deserve a place in the tabular anomaly detection toolbox as a non-redundant complementary view to the reconstruction-based scores that dominate current practice.
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