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A Johnson--Lindenstrauss Framework for Randomly Initialized CNNs
The celebrated Johnson--Lindenstrauss lemma answers this question for linear fully-connected neural networks (FNNs), stating that the geometry is essentially preserved.
A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs
The celebrated Johnson-Lindenstrauss lemma answers this question for linear fully-connected neural networks (FNNs), stating that the geometry is essentially preserved.
Monotonicity of the Trace-Inverse of Covariance Submatrices and Two-Sided Prediction
It is common to assess the "memory strength" of a stationary process looking at how fast the normalized log-determinant of its covariance submatrices (i. e., entropy rate) decreases.
Learning-based attacks in cyber-physical systems
In the case of scalar plants, we derive an upper bound on the attacker's deception probability for any measurable control policy when the attacker uses an arbitrary learning algorithm to estimate the system dynamics.
