C49 Example problem solving a system of linear DEs Part 1
Solving an example problem of a system of linear differential equations, where one of the equations is not homogeneous. It's a long problem, so this is only part 1.
From playlist Differential Equations
C50 Example problem solving a system of linear DEs Part 2
Part 2 of the prvious example problem, solving a system of linear differential equations, where one of the equations is non-homogeneous.
From playlist Differential Equations
B06 Example problem with separable variables
Solving a differential equation by separating the variables.
From playlist Differential Equations
A first example problem solving a linear, second-order, homogeneous, ODE with variable coefficients around a regular singular point.
From playlist Differential Equations
B07 Example problem with separable variables
Solving a differential equation by separating the variables.
From playlist Differential Equations
B04 Example problem with separable variables
Solving a differential equation by separating the variables.
From playlist Differential Equations
B05 Example problem with separable variables
Solving a differential equation by separating the variables.
From playlist Differential Equations
C51 Example problem of a system of linear DEs
Example problem solving a system of linear differential equations.
From playlist Differential Equations
Recovery of ridge functions in the uniform norm – Sebastian Mayer, Universität Bonn
Many problems in science and engineering involve an underlying unknown complex process that depends on a large number of parameters. The goal in many applications is to reconstruct, or learn, the unknown process given some direct or indirect observations. Mathematically, such a problem can
From playlist Approximating high dimensional functions
Efficient reasoning in PAC semantics - Brendan Juba
Brendan Juba Harvard University November 18, 2013 Machine learning is often employed as one step in a larger application, serving to perform information extraction or data mining for example. The rules obtained by such learning are then used as inputs to a further analysis. As a consequenc
From playlist Mathematics
DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models
This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent
From playlist Learning resources
Laurent Dinh: "A primer on normalizing flows"
Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "A primer on normalizing flows" Laurent Dinh, Google Abstract: Normalizing flows are a flexible family of probability distributions that ca
From playlist Machine Learning for Physics and the Physics of Learning 2019
Dominik Peters: Structural Tractability in Hedonic Games
Hedonic games are a well-studied model of coalition formation, in which selfish agents are partitioned into disjoint sets, and agents care about the make-up of the coalition they end up in. The computational problem of finding a stable outcome in a hedonic game tends to be computationally
From playlist HIM Lectures: Trimester Program "Combinatorial Optimization"
Considering a Career in AI Safety - Matthew Farrugia-Roberts
Matthew Farrugia-Roberts shares his general framework for thinking about choosing a career, how AI risk stacks up on those general criteria, and some of the reasons that have motivated him to choose AI safety as a field of investigation. He gives an overview of the problem of AI safety, ex
From playlist metauni festival 2023
Vacancy-induced local moments in frustrated magnets by Kedar Damle
DATES Monday 20 Jun, 2016 - Wednesday 29 Jun, 2016 VENUE Ramanujan Lecture Hall, ICTS Bangalore APPLY Understanding strongly interacting quantum many body systems is one of the major frontiers in present day physics. Condensed matter physics provides a wide panoply of systems where strong
From playlist School on Current Frontiers in Condensed Matter Research
A Complete Dichotomy Rises from the Capture of Vanishing Signatures - Jin-Yi Cai
Jin-Yi Cai University of Wisconsin November 19, 2012 Holant Problems are a broad framework to describe counting problems. The framework generalizes counting Constraint Satisfaction Problems and partition functions of Graph Homomorphisms. We prove a complexity dichotomy theorem for Holant
From playlist Mathematics
B23 Example problem solving for a homogeneous DE
The first substitution changes a DE in differential form that could not otherwise be solved (it is not exact, nor can it be changed into an exact equation by using an integrating factor) into a DE in which separation of variables can be applied. Make sure the DE is homogeneous, though.
From playlist Differential Equations
Daniel Kral: Parametrized approach to block structured integer programs
Integer programming is one of the most fundamental problems in discrete optimization. While integer programming is computationally hard in general, there exist efficient algorithms for special instances. In particular, integer programming is fixed parameter tractable when parameterized by
From playlist Workshop: Parametrized complexity and discrete optimization
Separable Differential Equations (Differential Equations 12)
https://www.patreon.com/ProfessorLeonard How to solve Separable Differential Equations by Separation of Variables. Lots of examples!!
From playlist Differential Equations
Standa Zivny: The Power of Sherali Adams Relaxations for General Valued CSPs
In this talk, we survey recent results on the power of LP relaxations (the basic LP relaxation and Sherali-Adams relaxations) in the context of valued constraint satisfaction problems (VCSP). We give precise characterisations of constraint languages for which these relaxations are exact, a
From playlist HIM Lectures 2015