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Found 6 papers, 2 papers with code
Sensor Placement for Flapping Wing Model Using Stochastic Observability Gramians
Systems in nature are stochastic as well as nonlinear.
Neural-inspired Measurement Observability
The neural encoding by biological sensors of flying insects, which prefilters stimulus data before sending it to the central nervous system in the form of voltage spikes, enables sensing capabilities that are computationally low-cost while also being highly robust to noise.
On the sensitivity of pose estimation neural networks: rotation parameterizations, Lipschitz constants, and provable bounds
We show that this measure is a type of Lipschitz constant, and that it is bounded by the product of a network's Euclidean Lipschitz constant and an intrinsic property of a rotation parameterization which we call the "distance ratio constant".
Observability Conditions and Sensing Quality for Unicycle Systems with Constant External Forcing
In certain systems which are subject to significant constant external forcing such as an airplane in wind or an underwater glider in ocean currents, the ability to detect the forcing depends on both the measurements available and whether appropriate control is being applied.
Analytical bounds on the local Lipschitz constants of ReLU networks
In this paper, we determine analytical upper bounds on the local Lipschitz constants of feedforward neural networks with ReLU activation functions.
Analytical bounds on the local Lipschitz constants of affine-ReLU functions
In this paper, we determine analytical bounds on the local Lipschitz constants of of affine functions composed with rectified linear units (ReLUs).
