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NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations

Neural Information Processing Systems

Each block represents atime-invariant iterativeprocess as the first layer in thei-th block,xi(1), is unrolled into a pattern-dependent number,Ki, of processing stages, using weight matricesAi andBi. The skip connections from the input,ui, to all layers in blockimake the process nonautonomous. Blocks can be chained together (each block modeling adifferent latent space) by passing final latentrepresentation,xi(Ki),ofblockiastheinputtoblocki+1.







AccurateLayerwiseInterpretableCompetence Estimation

Neural Information Processing Systems

Our contributions are twofold: First, we establish a statistically rigorous definition of competence that generalizesthecommon notion ofclassifier confidence; second, wepresent theALICE (Accurate Layerwise Interpretable Competence Estimation) Score, a pointwise competence estimator foranyclassifier.