Publications associées à Steady-state performance of incremental learning over distributed networks for non-Gaussian data

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Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Michaël Unser, Julien René Pierre Fageot, Emrah Bostan

We study the statistics of wavelet coefficients of non-Gaussian images, focusing mainly on the behaviour at coarse scales. We assume that an image can be whitened by a fractional Laplacian operator, which is consistent with an

∥ω∥ ^{ -\gamma }

spectral ...

IEEE2014

Steady-state performance of incremental learning over distributed networks for non-Gaussian data

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Adaptation and Learning Over Networks Under Subspace Constraints-Part I: Stability Analysis

On the 2D Post-Processing of Brillouin Optical Time-Domain Analysis

Distributed Inference Over Networks Under Subspace Constraints

Distributed Learning over Networks under Subspace Constraints

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling

Language Support for Distributed Functional Programming

Modeling the Importance of Faces in Natural Images

Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Diffusion LMS for source and process estimation in sensor networks

Solving the Shortest Vector Problem in 2^n Time via Discrete Gaussian Sampling

On the 2D Post-Processing of Brillouin Optical Time-Domain Analysis

Distributed Learning over Networks under Subspace Constraints

Language Support for Distributed Functional Programming

Adaptation and Learning Over Networks Under Subspace Constraints-Part I: Stability Analysis

Diffusion LMS for source and process estimation in sensor networks

Statistics of Wavelet Coefficients for Sparse Self-Similar Images

Distributed Inference Over Networks Under Subspace Constraints

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Modeling the Importance of Faces in Natural Images