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Found 9 papers, 2 papers with code
Structured Analysis Dictionary Learning for Image Classification
We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning.
Analysis Dictionary Learning based Classification: Structure for Robustness
A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification.
Demystifying Deep Learning: A Geometric Approach to Iterative Projections
Parametric approaches to Learning, such as deep learning (DL), are highly popular in nonlinear regression, in spite of their extremely difficult training with their increasing complexity (e. g. number of layers in DL).
Deep Dictionary Learning: A PARametric NETwork Approach
Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics.
Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks
In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i. e. transferring) knowledge from sensor data and enhancing low-resolution target detection.
Analysis Dictionary Learning: An Efficient and Discriminative Solution
Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems.
Community Detection and Improved Detectability in Multiplex Networks
We further demonstrate by numerical experiments that in the presence of consistent communities between different layers, consistent communities are matched, and the detectability is improved over a single layer.
A New Parameterized Family of Stochastic Particle Flow Filters
We prove that the particle flows are unbiased under the assumption of linear measurement and Gaussian distributions, and that estimates constructed from the stochastic flows are consistent.
Stability and Convergence of Stochastic Particle Flow Filters
In this paper, we examine dynamic properties of particle flows for a recently derived parameterized family of stochastic particle flow filters for nonlinear filtering and Bayesian inference.
