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A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs
It is however unclear how to extend these measures to DNNs and therefore the existing analyses are applicable to simple neural networks, which are not used in practice, e. g., linear or shallow ones or otherwise multi-layer perceptrons.
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle).
LSALSA: Accelerated Source Separation via Learned Sparse Coding
We propose an efficient algorithm for the generalized sparse coding (SC) inference problem.
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