Bifurcation models represent set-valued solution maps via weight-tied equilibrium dynamics whose attractors encode multiple solutions, with a proof that broad locally Lipschitz set-valued maps admit regular dynamical representations and experiments showing label-free discovery of multiple equilibria
Lipschitz bounded equilib- rium networks
4 Pith papers cite this work, alongside 24 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.
A direct parameterization ensures well-posedness of rational LPV-LFR models and supports joint estimation of the LPV plant and scheduling map from input-output data alone.
A contraction-theory separation principle yields global exponential stability for controller-observer pairs and sharp LMI certificates for contractive RNNs, enabling stable output tracking and implicit neural network design.
citing papers explorer
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Bifurcation Models: Learning Set-Valued Solution Maps with Weight-Tied Dynamics
Bifurcation models represent set-valued solution maps via weight-tied equilibrium dynamics whose attractors encode multiple solutions, with a proof that broad locally Lipschitz set-valued maps admit regular dynamical representations and experiments showing label-free discovery of multiple equilibria
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LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.
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Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
A direct parameterization ensures well-posedness of rational LPV-LFR models and supports joint estimation of the LPV plant and scheduling map from input-output data alone.
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A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
A contraction-theory separation principle yields global exponential stability for controller-observer pairs and sharp LMI certificates for contractive RNNs, enabling stable output tracking and implicit neural network design.