Natural Models of Type Theory - Steve Awodey
Steve Awodey Carnegie Mellon University; Member, School of Mathematics March 28, 2013 For more videos, visit http://video.ias.edu
From playlist Mathematics
Model Theory - part 01 - The Setup in Classical Set Valued Model Theory
Here we give the basic setup for Model Theory. I learned this from a talk Tom Scanlon gave in 2010 at CUNY.
From playlist Model Theory
Model Theory - part 03 - Terms, Formulas, Sequents
He we are a little bit more precise about keeping track of what fragments of formal languages we are using. This becomes relevant when you want to interpret them later. Caramello's book was useful in preparing this. We also found the post on nCatLab useful.
From playlist Model Theory
Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
Model Theory - part 08 - Syntactic Catgories
These are the categories where functors from these dudes are models... these take forever to define. On top of it, these end up just being friggin' definable sets and definable morphisms!! I remember being a place in here where there is a diagram which i say commutes but I need to actually
From playlist Model Theory
Eric Vanden-Eijnden (DDMCS@Turing): Neural networks as interacting particle systems
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
Model Theory - part 07 - Semantics pt 1
This is the first video on semantics.
From playlist Model Theory
Sebastian Reich (DDMCS@Turing): Learning models by making them interact
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
(PP 6.2) Multivariate Gaussian - examples and independence
Degenerate multivariate Gaussians. Some sketches of examples and non-examples of Gaussians. The components of a Gaussian are independent if and only if they are uncorrelated.
From playlist Probability Theory
Wolfram Summer School Physics Track Opening Keynote
Stephen wolfram gives his opening keynote for the Wolfram Summer School physics track. Find more information about the summer school here: https://education.wolfram.com/summer/school/ Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check out the announceme
From playlist Wolfram Physics Project Livestream Archive
Phiala Shanahan Public Lecture: The Building Blocks of the Universe
In her public lecture at Perimeter Institute on Wednesday Nov. 7, Phiala Shanahan will provided a guided tour of the subatomic realm and described what supercomputer calculations of quarks and gluons can reveal about the origins of mass, the primordial nuclear reactions that power the Sun,
From playlist Public Lecture Series
Univalence from a computer science point-of-view - Dan Licata
Vladimir Voevodsky Memorial Conference Topic: Univalence from a computer science point-of-view Speaker: Dan Licata Affiliation: Wesleyan University Date: September 14, 2018 For more video please visit http://video.ias.edu
From playlist Mathematics
The Sachdev-Ye-Kitaev quantum mechanics model, black holes, and random matrices - Douglas Stanford
The Sachdev-Ye-Kitaev quantum mechanics model, black holes, and random matrices Douglas Stanford Member, School of Natural Sciences October 26, 2016
From playlist Mathematics
Qubit Regularization of Asymptotic Freedom by Shailesh Chandrasekharan
PROGRAM Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography (ONLINE) ORGANIZERS: David Berenstein (UCSB), Simon Catterall (Syracuse University), Masanori Hanada (University of Surrey), Anosh Joseph (IISER, Mohali), Jun Nishimura (KEK Japan), David Sc
From playlist Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography (Online)
Vivien Kendon: How to compute using quantum walks
Quantum walks are widely and successfully used to model diverse physical processes. This leads to computation of the models, to explore their properties. Quantum walks have also been shown to be universal for quantum computing. This is a more subtle result than is often appreciated, since
From playlist Numerical Analysis and Scientific Computing
Lattice Holographic Cosmology by Kostas Skenderis
PROGRAM Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography (ONLINE) ORGANIZERS: David Berenstein (UCSB), Simon Catterall (Syracuse University), Masanori Hanada (University of Surrey), Anosh Joseph (IISER, Mohali), Jun Nishimura (KEK Japan), David Sc
From playlist Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography (Online)
Supermatrix Models - R. Dijkgraaf - 2/24/2015
Introduction by Sergei Gukov. Learn more about the Inaugural Celebration and Symposium of the Walter Burke Institute for Theoretical Physics: https://burkeinstitute.caltech.edu/workshops/Inaugural_Symposium Produced in association with Caltech Academic Media Technologies. ©2015 Californi
From playlist Walter Burke Institute for Theoretical Physics - Dedication and Inaugural Symposium - Feb. 23-24, 2015
New Approaches to the Hierarchy Problem I - Nathaniel Craig
Prospects in Theoretical Physics Particle Physics at the LHC and Beyond Topic: New Approaches to the Hierarchy Problem II Speaker: Nathaniel Craig Date: July 18, 2017
From playlist PiTP 2017
(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
From playlist Probability Theory