White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?

Revisiting RIP Guarantees for Sketching Operators on Mixture Models

Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning

Scalable Deep Compressive Sensing

Deep learning has been used to image compressive sensing (CS) for enhanced reconstruction performance. However, most existing deep learning methods train different models for different subsampling...