An introduction to Invariant Theory - Harm Derksen
Optimization, Complexity and Invariant Theory Topic: An introduction to Invariant Theory Speaker: Harm Derksen Affiliation: University of Michigan Date: June 4, 2018 For more videos, please visit http://video.ias.edu
From playlist Mathematics
Matrix invariants and algebraic complexity theory - Harm Derksen
Computer Science/Discrete Mathematics Seminar I Topic: Matrix invariants and algebraic complexity theory Speaker: Harm Derksen More videos on http://video.ias.edu
From playlist Mathematics
Commutative algebra 4 (Invariant theory)
This lecture is part of an online course on commutative algebra, following the book "Commutative algebra with a view toward algebraic geometry" by David Eisenbud. This lecture is an informal historical summary of a few results of classical invariant theory, mainly to show just how complic
From playlist Commutative algebra
Introduction to geometric invariant theory 1: Noncommutative duality - Ankit Garg
Optimization, Complexity and Invariant Theory Topic: Introduction to geometric invariant theory 1: Noncommutative duality Speaker: Ankit Garg Affiliation: Microsoft Research New England Date: June 5. 2018 For more videos, please visit http://video.ias.edu
From playlist Mathematics
Symmetries show up everywhere in physics. But what is a symmetry? While the symmetries of shapes can be interesting, a lot of times, we are more interested in symmetries of space or symmetries of spacetime. To describe these, we need to build "invariants" which give a mathematical represen
From playlist Relativity
Algorithmic invariant theory - Visu Makam
Optimization, Complexity and Invariant Theory Topic: Algorithmic invariant theory Speaker: Visu Makam Affiliation: University of Michigan Date: June 6. 2018 For more videos, please visit http://video.ias.edu
From playlist Mathematics
16. Invariants questions 27-29
This was an interesting experience. The first question I saw immediately how to do, as, at least for someone with the right background in elementary number theory, it was a genuinely straightforward question. The second looked pretty hard, but I happened to play around with it in a fruitfu
From playlist Thinking about maths problems in real time: mostly invariants problems
From shallow to deep learning for inverse imaging problems - Carola-Bibiane Schönlieb, Cambridge
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
Maths for Programmers: Introduction (What Is Discrete Mathematics?)
Transcript: In this video, I will be explaining what Discrete Mathematics is, and why it's important for the field of Computer Science and Programming. Discrete Mathematics is a branch of mathematics that deals with discrete or finite sets of elements rather than continuous or infinite s
From playlist Maths for Programmers
Limit Theorems in Pseudorandomness - Raghu Meka
Raghu Meka The University of Texas at Austin; Member, School of Mathematics October 3, 2011 For more videos, visit http://video.ias.edu
From playlist Mathematics
Inference: A Logical-Philosophical Perspective - Moderated Conversation w/ A.C. Paseau and Gila Sher
Inference: A Logical-Philosophical Perspective. Moderated Conversation with Gila Sher, Department of Philosophy, University of California, San Diego on the talk by Alexander Paseau, Faculty of Philosophy, University of Oxford. The Franke Program in Science and the Humanities Understandi
From playlist Franke Program in Science and the Humanities
Wolfram Physics Project Launch
Stephen Wolfram publicly kicks off an ambitious new project to find the Fundamental Theory of Physics. Begins at 2:50 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website: https://wolfr.am/physics Check out the announceme
From playlist Wolfram Physics Project Livestream Archive
Physics inspired algorithms by Nisheeth Vishnoi
DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n
From playlist Statistical Physics of Machine Learning 2020
Week 7 - Symmetry and Equivariance in Neural Networks - Tess Smidt
More about this lecture: https://dl4sci-school.lbl.gov/tess-smidt Deep Learning for Science School: https://dl4sci-school.lbl.gov/agenda
From playlist ML & Deep Learning
Professor Stéphane Mallat: "High-Dimensional Learning and Deep Neural Networks"
The Turing Lectures: Mathematics - Professor Stéphane Mallat: High-Dimensional Learning and Deep Neural Networks Click the below timestamps to navigate the video. 00:00:07 Welcome by Professor Andrew Blake, Director, The Alan Turing Institute 00:01:36 Introduction by Professo
From playlist Turing Lectures
Title: Rational Invariants of Finite Abelian Groups and Their Applications
From playlist Spring 2016
Nonlinear algebra, Lecture 10: "Invariant Theory", by Bernd Sturmfels
This is the tenth lecture in the IMPRS Ringvorlesung, the advanced graduate course at the Max Planck Institute for Mathematics in the Sciences.
From playlist IMPRS Ringvorlesung - Introduction to Nonlinear Algebra
The mother of all representer theorems for inverse problems & machine learning - Michael Unser
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
Gábor Csányi: "Representation and regression problems in molecular structure and dynamics"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Representation and regression problems in molecular structure and dynamics" Gábor Csányi - University of Cambridge Abstract: A vast proportion of total global comput
From playlist Machine Learning for Physics and the Physics of Learning 2019