Symposium on Compressed Sensing, Deep Learning

Connections between sparse auto encoders and sparse representation

Sparse coding, sparse representations, and dictionary learning

Sparse and low-rank approximation algorithms

Learning on graphs

Connections between learning rate and sampling rate

Sparsity and super resolution.

Recurrent neural networks for periodic and quasi-periodic signals

Phase retrieval and bilinear problems

Tensor sketching and factorizations

Compressed learning – compressive sensing for learning: new theory and methods

Dimensionality reduction, feature extraction, classification, detection, and source separation

Geometric wavelet theory

Sparsity measures in approximation theory, information theory and statistics

Regularization theory with low-complexity / low-dimensional structures

Statistical models and algorithms for sparsity

Sparse network theory and analysis

End-to-end deep-learning pattern recognition systems

Advanced supervised and unsupervised deep-learning algorithms

Deep-learning software and hardware architecture

Big data applications