Basic stochastic simulation b: Stochastic simulation algorithm
(C) 2012-2013 David Liao (lookatphysics.com) CC-BY-SA Specify system Determine duration until next event Exponentially distributed waiting times Determine what kind of reaction next event will be For more information, please search the internet for "stochastic simulation algorithm" or "kin
From playlist Probability, statistics, and stochastic processes
Introduction to the paper https://arxiv.org/abs/2002.06707
From playlist Research
Gilles Pagès: Optimal vector Quantization: from signal processing to clustering and ...
Abstract: Optimal vector quantization has been originally introduced in Signal processing as a discretization method of random signals, leading to an optimal trade-off between the speed of transmission and the quality of the transmitted signal. In machine learning, similar methods applied
From playlist Probability and Statistics
IDTIMWYTIM: Stochasticity - THAT'S Random
Hank helps us understand the difference between the colloquial meaning of randomness, and the scientific meaning, which is also known as stochasticity. We will learn how, in fact, randomness is surprisingly predictable. Like SciShow: http://www.facebook.com/scishow Follow SciShow: http://
From playlist Uploads
Prob & Stats - Markov Chains (8 of 38) What is a Stochastic Matrix?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a stochastic matrix. Next video in the Markov Chains series: http://youtu.be/YMUwWV1IGdk
From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes
The Most Powerful Tool Based Entirely On Randomness
We see the effects of randomness all around us on a day to day basis. In this video we’ll be discussing a couple of different techniques that scientists use to understand randomness, as well as how we can harness its power. Basically, we'll study the mathematics of randomness. The branch
From playlist Classical Physics by Parth G
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
Matrix models with chemical potential by Pallab Basu
Bangalore Area Strings Meeting - 2017 TIME : 31 July 2017 to 02 August 2017 VENUE:Madhava Lecture Hall, ICTS Bangalore Bengaluru now has a large group of string theorists, with 9 faculty members in the area, between ICTS and IISc. This is apart from a large group of postdocs and graduate
From playlist Bangalore Area Strings Meeting - 2017
Gilles Pagès: CVaR hedging using quantization based stochastic approximation algorithm
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 Analysis and its Applications
Andreas H. Hamel: From set-valued quantiles to risk measures: a set optimization approach to...
Abstract : Some questions in mathematics are not answered for quite some time, but just sidestepped. One of those questions is the following: What is the quantile of a multi-dimensional random variable? The "sidestepping" in this case produced so-called depth functions and depth regions, a
From playlist Probability and Statistics
Speech and Audio Processing 4: Speech Coding I - Professor E. Ambikairajah
Speech and Audio Processing Speech Coding - Lecture notes available from: http://eemedia.ee.unsw.edu.au/contents/elec9344/LectureNotes/
From playlist ELEC9344 Speech and Audio Processing by Prof. Ambikairajah
Eulalia Nualart: Asymptotics for some non-linear stochastic heat equations
Abstract: Consider the following stochastic heat equation, ∂ut(x)/∂t = −ν(−Δ)α/2ut(x)+σ(ut(x))F˙(t,x),t[is greater than]0,x∈ℝd. Here −ν(−Δ)α/2 is the fractional Laplacian with ν[is greater than]0 and α∈(0,2], σ:ℝ→ℝ is a globally Lipschitz function, and F˙(t,x) is a Gaussian noise which is
From playlist Probability and Statistics
Francois Baccelli: High dimensional stochastic geometry in the Shannon regime
This talk will focus on Euclidean stochastic geometry in the Shannon regime. In this regime, the dimension n of the Euclidean space tends to infinity, point processes have intensities which are exponential functions of n, and the random compact of interest sets have diameters of order squa
From playlist Workshop: High dimensional spatial random systems
Artificial Intelligence per Kilowatt-hour: Max Welling, University of Amsterdam
Professor Welling is a research chair in Machine Learning at the University of Amsterdam and a Vice President Technologies at Qualcomm. He has a secondary appointment at the Canadian Institute for Advanced Research (CIFAR). He is co-founder of “Scyfer BV” a university spin-off in deep lear
From playlist AI for Social Good
Stochastic GW Background From the Early Universe (Lecture 4) by Shi Pi
PROGRAM ICTS SUMMER SCHOOL ON GRAVITATIONAL-WAVE ASTRONOMY (ONLINE) ORGANIZERS: Parameswaran Ajith (ICTS-TIFR, India), K. G. Arun (CMI, India), Bala R. Iyer (ICTS-TIFR, India) and Prayush Kumar (ICTS-TIFR, India) DATE : 05 July 2021 to 16 July 2021 VENUE : Online This school is part
From playlist ICTS Summer School on Gravitational-Wave Astronomy (ONLINE)
Trace Dynamics: Quantum theory as an emergent phenomenon by Tejinder Singh ( Lecture - 02)
21 November 2016 to 10 December 2016 VENUE Ramanujan Lecture Hall, ICTS Bangalore Quantum Theory has passed all experimental tests, with impressive accuracy. It applies to light and matter from the smallest scales so far explored, up to the mesoscopic scale. It is also a necessary ingredie
From playlist Fundamental Problems of Quantum Physics
Tom Goldstein: "What do neural loss surfaces look like?"
New Deep Learning Techniques 2018 "What do neural loss surfaces look like?" Tom Goldstein, University of Maryland Abstract: Neural network training relies on our ability to find “good” minimizers of highly non-convex loss functions. It is well known that certain network architecture desi
From playlist New Deep Learning Techniques 2018
Hao Shen (Wisconsin) -- Stochastic quantization, large N, and mean field limit
We study "large N problems” in quantum field theory using SPDE methods via stochastic quantization. In the SPDE setting this is formulated as mean field problems. We will consider the vector Phi^4 model (i.e. linear sigma model), whose stochastic quantization is a system of N coupled dynam
From playlist Columbia Probability Seminar
Some success stories in bridging theory and practice in ML (Lecture 1) by Anima Anandkumar
DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr
From playlist The Theoretical Basis of Machine Learning 2018 (ML)