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

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

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that require the objectives across the network to lie in low-dimensional ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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

Luc Thévenaz, Zhisheng Yang, Simon Adrien Zaslawski

The benefits and limitations inherent to the 2D post-processing of measurements from Brillouin optical time-domain analyzers are investigated from a fundamental point of view. In a preliminary step, the impact of curve fitting on the precision of the estim ...

2020

Distributed Inference Over Networks Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that enforce the objectives across the network to lie in a low-dimensiona ...

IEEE2019

Distributed Learning over Networks under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This work presents and studies a distributed algorithm for solving optimization problems over networks where agents have individual costs to minimize subject to subspace constraints that require the minimizers across the network to lie in a low-dimensional ...

IEEE2019

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Ali H. Sayed, Jie Chen

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In ...

2017

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

Divesh Aggarwal

We give a randomized 2^{n+o(n)}-time and space algorithm for solving the Shortest Vector Problem (SVP) on n-dimensional Euclidean lattices. This improves on the previous fastest algorithm: the deterministic O(4^n)-time and O(2^n)-space algorithm of Miccian ...

ACM2015

Language Support for Distributed Functional Programming

Software development has taken a fundamental turn. Software today has gone from simple, closed programs running on a single machine, to massively open programs, patching together user experiences byway of responses received via hundreds of network requests ...

EPFL2015

Modeling the Importance of Faces in Natural Images

Sabine Süsstrunk, Cheryl Shaulya Lau, Appu Shaji, Gökhan Yildirim, Bin Jin

In this work we study the varying importance of faces in images. Face importance is found to be affected by the size and number of faces present. We collected a dataset of 152 face images with faces in different size and number of faces. We conducted a cro ...

Spie-Int Soc Optical Engineering2015

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

Diffusion LMS for source and process estimation in sensor networks

Ali H. Sayed

We develop a least mean-squares (LMS) diffusion strategy for sensor network applications where it is desired to estimate parameters of physical phenomena that vary over space. In particular, we consider a regression model with space-varying parameters that ...

IEEE2012

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

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

Language Support for Distributed Functional Programming

Distributed Inference Over Networks Under Subspace Constraints

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

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

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

Distributed Learning over Networks under Subspace Constraints

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