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Searching Results for ND6.4-15.07:
Sparsity, Emmanuel Candes  (California Institute of Technology)
Signal encoding, Ronald DeVore  (University of South Carolina)
Short presentations by participants (1st), Presentation 1
Short presentations by participants (2nd), Presentation 2
Short presentations by participants (3rd), Presentation 3
Short presentations by participants (4th), Presentation 4
Short presentations by participants (5th), Presentation 5
Short presentations by participants (6th), Presentation 6
Introduction to MRI, Leon Axel  (New York University), Steen Moeller  (University of Minnesota)
Sparsity and the l1 norm, Emmanuel Candes  (California Institute of Technology)
Compression, Ronald DeVore  (University of South Carolina)
Compressive sampling: Sparsity and incoherence, Emmanuel Candes  (California Institute of Technology)
Discrete compressed sensing, Ronald DeVore  (University of South Carolina)
The uniform uncertainty principle, Emmanuel Candes  (California Institute of Technology)
The restricted isometry property (RIP), Ronald DeVore  (University of South Carolina)
Algorithms for Compressed Sensing, I, Anna Gilbert  (University of Michigan)
The role of probability in compressive sampling, Emmanuel Candes  (California Institute of Technology)
Construction of CS matrices with best RIP, Ronald DeVore  (University of South Carolina)
Algorithms for Compressed Sensing, II, Anna Gilbert  (University of Michigan)
Robust compressive sampling and connections with statistics, Emmanuel Candes  (California Institute of Technology)
Performance of CS matrices revisited, Ronald DeVore  (University of South Carolina)
An introduction to transform coding (continued), Richard Baraniuk  (Rice University)
An introduction to transform coding, Richard Baraniuk  (Rice University)
Compressive sensing for time signals: Analog to information conversion, Richard Baraniuk  (Rice University)
Compressive sensing for detection and classification problems, Richard Baraniuk  (Rice University)
Robust compressive sampling and connections with statistics (continued), Emmanuel Candes  (California Institute of Technology)
Performance in probability, Ronald DeVore  (University of South Carolina)
Multi-signal, distributed compressive sensing, Richard Baraniuk  (Rice University)
Connections with information and coding theory, Emmanuel Candes  (California Institute of Technology)
Compressive imaging with a single pixel camera, Richard Baraniuk  (Rice University)
Decoders, Ronald DeVore  (University of South Carolina)
Modern convex optimization, Emmanuel Candes  (California Institute of Technology)
Performance of iterated least squares, Ronald DeVore  (University of South Carolina)
Applications, experiments and open problems, Emmanuel Candes  (California Institute of Technology)
Deterministic constructions of CS Matrices, Ronald DeVore  (University of South Carolina)
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