Compressed sensing

A Compressed Overview of Sparsity

This talk presents a high level overview of compressed sensing, especially as it relates to engineering applied mathematics. We provide context for sparsity and compression, followed by good rules of thumb and key ingredients to apply compressed sensing.

From playlist Research Abstracts from Brunton Lab

Compressed Sensing: Mathematical Formulation

This video introduces the mathematical theory of compressed sensing, related to high-dimensional geometry, robust statistics, and optimization. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow Chapter 3 from: "Data-Driven Science a

From playlist Sparsity and Compression [Data-Driven Science and Engineering]

Compressed Sensing: Overview

This video introduces compressed sensing, which is an exciting new branch of applied mathematics, making it possible to reconstruct full images from a random subset of the pixels. There is a ton of beautiful math behind this concept, touching on high-dimensional geometry, robust statistic

From playlist Sparsity and Compression [Data-Driven Science and Engineering]

Compressed Sensing: When It Works

This video provides conditions on when compressed sensing will work to reconstruct a full image from a random subsample of pixels. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow Chapter 3 from: "Data-Driven Science and Engineering

From playlist Sparsity and Compression [Data-Driven Science and Engineering]

Ben Adcock: Compressed sensing and high-dimensional approximation: progress and challenges

Abstract: Many problems in computational science require the approximation of a high-dimensional function from limited amounts of data. For instance, a common task in Uncertainty Quantification (UQ) involves building a surrogate model for a parametrized computational model. Complex physica

From playlist Numerical Analysis and Scientific Computing

Computational Linear Algebra 7: Compressed Sensing for CT Scans

Course materials available here: https://github.com/fastai/numerical-linear-algebra Compressed sensing is critical to allowing CT scans with lower radiation-- the image can be reconstructed with less data. Here we will learn the technique and apply it to CT images. Numpy Broadcasting Spars

From playlist Computational Linear Algebra

Gauss Prize Lecture: Compressed sensing — from blackboard to bedside — David Donoho — ICM2018

Compressed sensing — from blackboard to bedside David Donoho Abstract: In 2017, next-generation Magnetic Resonance Imaging (MRI) devices by General Electric and Siemens received US Food and Drug Administration approval, allowing them to be used in the US Health care marketplace. This year

From playlist Special / Prizes Lectures

Beating Nyquist with Compressed Sensing

This video shows how it is possible to beat the Nyquist sampling rate with compressed sensing (code in Matlab). Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow Chapter 3 from: "Data-Driven Science and Engineering: Machine Learning,

From playlist Sparsity and Compression [Data-Driven Science and Engineering]

Compressed Sensing and Dynamic Mode Decomposition

This video illustrates how to leverage compressed sensing to compute the dynamic mode decomposition (DMD) from under-sampled or compressed data. From the Paper: Compressed Sensing and Dynamic Mode Decomposition. JCD 2(2):165—191, 2015. Steven L. Brunton, Joshua L. Proctor, Jonathan H.

From playlist Research Abstracts from Brunton Lab

Structured Regularization Summer School - A.Hansen - 1/4 - 19/06/2017

Anders Hansen (Cambridge) Lectures 1 and 2: Compressed Sensing: Structure and Imaging Abstract: The above heading is the title of a new book to be published by Cambridge University Press. In these lectures I will cover some of the main issues discussed in this monograph/textbook. In par

From playlist Structured Regularization Summer School - 19-22/06/2017

Measurements vs. Bits: Compressed Sensors and Info Theory

October 18, 2006 lecture by Dror Baron for the Stanford University Computer Systems Colloquium (EE 380). Dror Baron discusses the numerous rich insights information theory has to offer Compressed Sensing (CS), an emerging field based on the revelation that optimization routines can reco

From playlist Course | Computer Systems Laboratory Colloquium (2006-2007)

Aymeric Dieuleveut - Federated Learning with Communication Constraints: Challenges in (...)

In this presentation, I will present some results on optimization in the context of federated learning with compression. I will first summarise the main challenges and the type of results the community has obtained, and dive into some more recent results on tradeoffs between convergence an

From playlist 8th edition of the Statistics & Computer Science Day for Data Science in Paris-Saclay, 9 March 2023

Holger Rauhut: Compressive sensing with time-frequency structured random matrices

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b

From playlist 30 years of wavelets

On the (unreasonable) effectiveness of compressive imaging – Ben Adcock, Simon Fraser University

This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together leading researchers in the general fields of mathematics, statistics, computer science and engineering. About the event The workshop ai

From playlist Mathematics of data: Structured representations for sensing, approximation and learning

Paul Hand - Signal Recovery with Generative Priors - IPAM at UCLA

Recorded 29 November 2022. Paul Hand of Northeastern University presents "Signal Recovery with Generative Priors" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Recovering images from very few measurements is an important task in imaging problems. Doing s

From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling

Sparse Sensor Placement Optimization for Classification

This video discusses the important problem of how to select the fewest and most informative sensors for a classification problem. I will discuss the algorithm and give several examples. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures fo

From playlist Sparsity and Compression [Data-Driven Science and Engineering]

Structured Regularization Summer School - A.Hansen - 2/4 - 19/06/2017

Anders Hansen (Cambridge) Lectures 1 and 2: Compressed Sensing: Structure and Imaging Abstract: The above heading is the title of a new book to be published by Cambridge University Press. In these lectures I will cover some of the main issues discussed in this monograph/textbook. In par

From playlist Structured Regularization Summer School - 19-22/06/2017