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Found 7 papers, 1 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).
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.
Symbolic Analysis-based Reduced Order Markov Modeling of Time Series Data
In the second example, the data set is taken from NASA's data repository for prognostics of bearings on rotating shafts.
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification
In this paper, we propose a supervised dictionary learning algorithm in the kernel domain for hyperspectral image classification.
Multimodal Task-Driven Dictionary Learning for Image Classification
Dictionary learning algorithms have been successfully used for both reconstructive and discriminative tasks, where an input signal is represented with a sparse linear combination of dictionary atoms.
Quality-based Multimodal Classification Using Tree-Structured Sparsity
An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information.
