Emmanuel Candès: Wavelets, sparsity and its consequences
Abstract: Soon after they were introduced, it was realized that wavelets offered representations of signals and images of interest that are far more sparse than those offered by more classical representations; for instance, Fourier series. Owing to their increased spatial localization at f
From playlist Abel Lectures
Sparsity and Parsimonious Models: Everything should be made as simple as possible, but no simpler
Sparsity has been a standard tool for discovering physical models for centuries, using the principle of Occam's razor. Here, we explore the history of parsimonious modeling, including Aristotle, Occam, Pareto, Newton, and Einstein. These lectures follow Chapter 3 from: "Data-Driven Scie
From playlist Sparsity and Compression [Data-Driven Science and Engineering]
Trigonometry 5 The Cosine Relationship
A geometrical explanation of the law of cosines.
From playlist Trigonometry
What causes the Pauli Exclusion Principle?
Explains exchange forces between identical particles and the origin of the Pauli Exclusion Principle. My Patreon page is at https://www.patreon.com/EugeneK
From playlist Physics
Solution to problems dealing with the Doppler effect.
From playlist Physics - Waves
Solution to problems dealing with the Doppler effect.
From playlist Physics - Waves
Graph and Subgraph Sparsification and its Implications to Linear System Solving... - Alex Kolla
Alexandra Kolla Institute for Advanced Study November 10, 2009 I will first give an overview of several constructions of graph sparsifiers and their properties. I will then present a method of sparsifying a subgraph W of a graph G with optimal number of edges and talk about the implicatio
From playlist Mathematics
Here, I define sparsity mathematically. Follow @eigensteve on Twitter These lectures follow Chapter 3 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz Amazon: https://www.amazon.com/Data-Driven-Science-Engineering-Lear
From playlist Sparsity and Compression [Data-Driven Science and Engineering]
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
Active Dendrites avoid catastrophic forgetting - Interview with the Authors
#multitasklearning #biology #neuralnetworks This is an interview with the paper's authors: Abhiram Iyer, Karan Grewal, and Akash Velu! Paper Review Video: https://youtu.be/O_dJ31T01i8 Check out Zak's course on Graph Neural Networks (discount with this link): https://www.graphneuralnets.c
From playlist General Machine Learning
David Donoho's Gauss Prize Laudatio — Emmanuel Candes — ICM2018
The work of David Donoho Emmanuel Candes ICM 2018 - International Congress of Mathematicians © www.icm2018.org Os direitos sobre todo o material deste canal pertencem ao Instituto de Matemática Pura e Aplicada, sendo vedada a utilização total ou parcial do conteúdo sem autorização pré
From playlist Special / Prizes Lectures
Bruno Olshausen: "From Natural Scene Statistics to Models of Neural Coding & Representation, Pt. 1"
Graduate Summer School 2012: Deep Learning, Feature Learning "From Natural Scene Statistics to Models of Neural Coding & Representation, Pt. 1" Bruno Olshausen, UC Berkeley Institute for Pure and Applied Mathematics, UCLA July 24, 2012 For more information: https://www.ipam.ucla.edu/pro
From playlist GSS2012: Deep Learning, Feature Learning
Hengrui Luo (7/27/20): Generalized penalty for circular coordinate representation
Title: Generalized penalty for circular coordinate representation Abstract: Topological Data Analysis (TDA) provides novel approaches that allow us to analyze the geometrical shapes and topological structures of a dataset. As one important application, TDA can be used for data visualizati
From playlist ATMCS/AATRN 2020
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
Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series
The Turing Lectures - Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series. Click the below timestamps to navigate the video. 00:00:12 Welcome & Introduction by Doctor Ioanna Manolopoulou 00:01:19 Professor Mike West: Structured Dynamic
From playlist Turing Lectures
Structured Regularization Summer School - C. Boyer - 22/06/2017
Claire Boyer (UPMC) Towards realistic compressed sensing Abstract: First, we will theoretically justify the applicability of compressed sensing (CS) in real-life applications. To do so, CS theorems compatible with physical acquisition constraints will be presented. These new results do n
From playlist Structured Regularization Summer School - 19-22/06/2017
Fourth Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Date: Wednesday, November 4, 10:00am EDT Speaker: Daniela Calvetti, Case Western Reserve University Title: Bayesian reimaging of sparsity in inverse problems. Abstract: The recovery of sparse generative models from few noisy measurements is a challenging inverse problem with application
From playlist Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series
(ML 19.2) Existence of Gaussian processes
Statement of the theorem on existence of Gaussian processes, and an explanation of what it is saying.
From playlist Machine Learning