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Found 8 papers, 1 papers with code
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising
Thanks to the maximum a posteriori Bayesian framework, such regularizer can be systematically linked with the distribution of the data.
Fast off-the-grid sparse recovery with over-parametrized projected gradient descent
We consider the problem of recovering off-the-grid spikes from Fourier measurements.
A theory of optimal convex regularization for low-dimensional recovery
To perform recovery, we consider the minimization of a convex regularizer subject to a data fit constraint.
Projected gradient descent for non-convex sparse spike estimation
We propose a new algorithm for sparse spike estimation from Fourier measurements.
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-scale learning that we introduced in a companion paper. The principle of compressive statistical learning is to compress a training collection, in one pass, into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task.
Compressive Statistical Learning with Random Feature Moments
We describe a general framework -- compressive statistical learning -- for resource-efficient large-scale learning: the training collection is compressed in one pass into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task.
Compressive K-means
We demonstrate empirically that CKM performs similarly to Lloyd-Max, for a sketch size proportional to the number of cen-troids times the ambient dimension, and independent of the size of the original dataset.
Phase Unmixing : Multichannel Source Separation with Magnitude Constraints
We consider the problem of estimating the phases of K mixed complex signals from a multichannel observation, when the mixing matrix and signal magnitudes are known.
