Did you mean: Non Invertibility in neural networks.
Most obvious neural network architectures cannot possibly be invertible. Consider for example a classifier which takes an image or some other high-dimensional input, and outputs a classification label.
Apr 20, 2021
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Noninvertibility may also create problems when (noninvertible) neural networks are used in nonlinear discrete-time model-predictive control schemes ...
This paper presents illustrations of the pathologies that neural networks may exhibit due to their noninvertible character. These illustrations are based on two ...
We present and discuss an inherent shortcoming of neural networks used as discrete-time models in system identification, time.
Abstract: A method for assessing certain validity aspects of predictions made by neural networks used to approximate continuous (in time) dynamical systems ...
We present a method for assessing certain validity aspects of predictions made by neural networks used to approximate continuous (in time) dynamical systems ...
Dec 20, 2016 · This non-linearity in the parameters/variables comes about two ways: 1) having more than one layer with neurons in your network (as exhibited ...
Jun 26, 2019 · Invertible NN are just neural networks that represent bijective functions f. · Normalizing flows are invertible NN f that also have a tractable ...
Request PDF | Noninvertibility in neural networks | We present and discuss an inherent shortcoming of neural networks used as discrete-time models in system ...
Jul 31, 2019 · Activation functions can be invertible, but a neural network as a whole is not invertible in general, even with invertible activation functions.