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Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes
We propose a novel confidence scoring mechanism for deep neural networks based on a two-model paradigm involving a base model and a meta-model.
Anomaly Attribution with Likelihood Compensation
In either case, one would ideally want to compute a ``responsibility score'' indicative of the extent to which an input variable is responsible for the anomalous output.
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
In particular, we are interested in discovering instance-level causal structures in an unsupervised manner.
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