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Compelling ReLU Network Initialization and Training to Leverage Exponential Scaling with Depth
To address this issue, we introduce a novel training strategy: we first reparameterize the network weights in a manner that forces an exponential number of activation patterns to manifest.
Incorporating Data Uncertainty in Object Tracking Algorithms
However, for detections generated from neural-network processed camera inputs, these measurement error statistics are not sufficient to represent the primary source of errors, namely a dissimilarity between run-time sensor input and the training data upon which the detector was trained.
Testing for Typicality with Respect to an Ensemble of Learned Distributions
In particular, we propose training an ensemble of density models, considering data to be anomalous if the data is anomalous with respect to any member of the ensemble.
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