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Found 7 papers, 0 papers with code
A Dynamically Controlled Recurrent Neural Network for Modeling Dynamical Systems
This work proposes a novel neural network architecture, called the Dynamically Controlled Recurrent Neural Network (DCRNN), specifically designed to model dynamical systems that are governed by ordinary differential equations (ODEs).
Training products of expert capsules with mixing by dynamic routing
This study develops an unsupervised learning algorithm for products of expert capsules with dynamic routing.
Training capsules as a routing-weighted product of expert neurons
Capsules are the multidimensional analogue to scalar neurons in neural networks, and because they are multidimensional, much more complex routing schemes can be used to pass information forward through the network than what can be used in traditional neural networks.
Using natural conversations to classify autism with limited data: Age matters
Spoken language ability is highly heterogeneous in Autism Spectrum Disorder (ASD), which complicates efforts to identify linguistic markers for use in diagnostic classification, clinical characterization, and for research and clinical outcome measurement.
On Residual Networks Learning a Perturbation from Identity
It is found that, in this setting, the average scaled perturbation magnitude is roughly inversely proportional to increasing the number of residual blocks, and from this it follows that for sufficiently large residual networks, they are learning a perturbation from identity.
State Space Representations of Deep Neural Networks
This paper deals with neural networks as dynamical systems governed by differential or difference equations.
Principles of Riemannian Geometry in Neural Networks
This implies that the network is learning systems of differential equations governing the coordinate transformations that represent the data.
