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Found 6 papers, 3 papers with code
Lipschitz constant estimation for general neural network architectures using control tools
This paper is devoted to the estimation of the Lipschitz constant of neural networks using semidefinite programming.
State space representations of the Roesser type for convolutional layers
For this reason, we explicitly provide a state space representation of the Roesser type for 2-D convolutional layers with $c_\mathrm{in}r_1 + c_\mathrm{out}r_2$ states, where $c_\mathrm{in}$/$c_\mathrm{out}$ is the number of input/output channels of the layer and $r_1$/$r_2$ characterizes the width/length of the convolution kernel.
Convolutional Neural Networks as 2-D systems
This paper introduces a novel representation of convolutional Neural Networks (CNNs) in terms of 2-D dynamical systems.
Lipschitz constant estimation for 1D convolutional neural networks
In this work, we propose a dissipativity-based method for Lipschitz constant estimation of 1D convolutional neural networks (CNNs).
Neural network training under semidefinite constraints
This paper is concerned with the training of neural networks (NNs) under semidefinite constraints, which allows for NN training with robustness and stability guarantees.
Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliers
In this paper, we analyze the stability of feedback interconnections of a linear time-invariant system with a neural network nonlinearity in discrete time.
