Hamiltonian mechanics | Statistical models
An energy-based model (EBM) is a form of generative model (GM) imported directly from statistical physics to learning. GMs learn an underlying data distribution by analyzing a sample dataset. Once trained, a GM can produce other datasets that also match the data distribution. EBMs provide a unified framework for many probabilistic and non-probabilistic approaches to such learning, particularly for training graphical and other structured models. An EBM learns the characteristics of a target dataset and generates a similar but larger dataset. EBMs detect the latent variables of a dataset and generate new datasets with a similar distribution. Target applications include natural language processing, robotics and computer vision. (Wikipedia).
Your Brain on Energy-Based Models: Applying and Scaling EBMs to Problems...- Will Grathwohl
Seminar on Theoretical Machine Learning Topic: Your Brain on Energy-Based Models: Applying and Scaling EBMs to Problems of Interest to Machine Learning Speaker: Will Grathwohl Affiliation: University of Toronto Date: March 10, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Is renewable energy really sustainable?
Renewable energy is described as replenishable, safe for the environment, and available in the long term. But do all renewable energy sources meet these criteria? Watch to find out. Find out more information at https://bit.ly/3p0Thsz To get the latest science and technology news, su
From playlist Theory to Reality
Visualization of conservation of energy
See explanations for this video in https://www.udiprod.com/energy/
From playlist Animated Physics Simulations
Concept Learning with Energy-Based Models (Paper Explained)
This is a hard paper! Energy-functions are typically a mere afterthought in current machine learning. A core function of the Energy - its smoothness - is usually not exploited at inference time. This paper takes a stab at it. Inferring concepts, world states, and attention masks via gradie
From playlist Papers Explained
Teach Astronomy - Types of Energy
http://www.teachastronomy.com/ There are several broad types of energy. Energy is measured in units of calories in the English system or joules in the international system of metric units. One broad category of energy is kinetic energy, or the energy of motion. A second broad category of
From playlist 04. Chemistry and Physics
Kinetic Energy: Example Problems
This video gives an explanation of kinetic and contains several examples for calculating kinetic energy, mass and velocity using the kinetic energy equation. Kinetic energy is the energy an object possesses due to its motion. If an object is in motion then it has kinetic energy. It is als
From playlist Kinetic Energy, Potential Energy, Work, Power
Solar Powered Alarm Clock - Part 2
This is the wrap up of the Solar Powered Alarm Clock. I also introduce some new projects I will be working on.
From playlist Solar Powered Projects
Best Stirling Engine Free Energy from the Sun EXPLAINED What is a Stirling Engine
Free solar energy from the sun and a large Fresnel lens is focused on the Stirling Engine displacer to produce flywheel movement. http://greenpowerscience.com/ Stirling Engines are heat engines that can operate from Solar concentrated sunlight. Robert Stirling invented the Stirling Engin
From playlist TV SHOW VIDEOS GREENPOWERSCIENCE.COM GREEN POWER SCIENCE
Thermodynamics 3b - Energy and the First Law II
We apply the first law of thermodynamics to understand the Stirling cycle heat engine. Note on the definition of a "closed system." I am using the term "closed system" in the sense of the following definition from Thermal Physics by Charles Kittel: "A closed system is defined as a system
From playlist Thermodynamics
Yann LeCun | May 18, 2021 | The Energy-Based Learning Model
Title: The Energy-Based Learning Model Speaker: Yann LeCun Abstract: One of the hottest sub-topics of machine learning in recent times has been Self-Supervised Learning (SSL). In SSL, a learning machine captures the dependencies between input variables, some of which may be observed, denot
From playlist AI talks
DSI | Interpretability in deep learning models for atomic-scale simulations
Interpretability in deep learning models for atomic-scale simulations In the field of computational materials science, model interpretability has historically been enforced through the use of strictly defined functional forms based on assumptions regarding the underlying physics governing
From playlist DSI Virtual Seminar Series
Ronny Lorenz: Improving RNA secondary structure prediction
Recording during the meeting "AlgoSB 2019 - Mathematical and Computational Methods for Structured RiboNucleic Acids" the January 14, 2019 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by w
From playlist Mathematics in Science & Technology
Developing HEV Control Systems
Learn about HEV modeling and simulation. In this video you will: - Get an overview of HEV controls systems and the concept of energy management. - Understand the detailed control algorithm implementation in Simulink® and Stateflow®. - Test the controller and learn best practices. Downloa
From playlist Hybrid Electric Vehicles
On the critic function of implicit generative models - Arthur Gretton
Seminar on Theoretical Machine Learning Topic: On the critic function of implicit generative models Speaker: Arthur Gretton Affiliation: University College London Date: July 28, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
On the critic function of implicit generative models - Arthur Gretton
Seminar on Theoretical Machine Learning Topic: On the critic function of implicit generative models Speaker: Arthur Gretton Affiliation: University College London Date: July 28, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Yann LeCun - Self-Supervised Learning: The Dark Matter of Intelligence (FAIR Blog Post Explained)
#selfsupervisedlearning #yannlecun #facebookai Deep Learning systems can achieve remarkable, even super-human performance through supervised learning on large, labeled datasets. However, there are two problems: First, collecting ever more labeled data is expensive in both time and money.
From playlist Papers Explained
Physics 13.5.2a - Alternative Energy
A discussion of alternative energy sources, including wind, thermal, solar, and tides. From the high school physics course by Derek Owens.
From playlist Physics - Electric Circuits
Xiaoying Dai - Convergent orthogonality preserving appoximations of the Kohn-Sham orbitals
Recorded 05 May 2022. Xiaoying Dai of the Chinese Academy of Sciences presents "Convergent orthogonality preserving appoximations of the Kohn-Sham orbitals" at IPAM's Large-Scale Certified Numerical Methods in Quantum Mechanics Workshop. Abstract: To obtain convergent numerical approximati
From playlist 2022 Large-Scale Certified Numerical Methods in Quantum Mechanics