Continuous multi-fidelity optimization
This video is #8 in the Adaptive Experimentation series presented at the 18th IEEE Conference on eScience in Salt Lake City, UT (October 10-14, 2022). In this video, Sterling Baird @sterling-baird presents on continuous multifidelity optimization. Continuous multi-fidelity optimization is
From playlist Optimization tutorial
A very basic overview of optimization, why it's important, the role of modeling, and the basic anatomy of an optimization project.
From playlist Optimization
Discrete multi-fidelity optimization
This video is #9 in the Adaptive Experimentation series presented at the 18th IEEE Conference on eScience in Salt Lake City, UT (October 10-14, 2022). In this video, Sterling Baird @sterling-baird presents on discrete multi-fidelity optimization. In discrete multi-fidelity optimization, t
From playlist Optimization tutorial
How To Build User-Adaptive Interfaces
Users have indicated many preferences on their devices these days. They want the operating system and apps to look and feel like their own. User-adaptive interfaces are those which are ready to use these preferences to enhance the user experience, to make it feel more at home. If done corr
From playlist Web Design: CSS / SVG
Alina Ene: Adaptive gradient descent methods for constrained optimization
Adaptive gradient descent methods, such as the celebrated Adagrad algorithm (Duchi, Hazan, and Singer; McMahan and Streeter) and ADAM algorithm (Kingma and Ba), are some of the most popular and influential iterative algorithms for optimizing modern machine learning models. Algorithms in th
From playlist Workshop: Continuous approaches to discrete optimization
13_1 An Introduction to Optimization in Multivariable Functions
Optimization in multivariable functions: the calculation of critical points and identifying them as local or global extrema (minima or maxima).
From playlist Advanced Calculus / Multivariable Calculus
13_2 Optimization with Constraints
Here we use optimization with constraints put on a function whose minima or maxima we are seeking. This has practical value as can be seen by the examples used.
From playlist Advanced Calculus / Multivariable Calculus
Adaptive Quadrature | Lecture 41 | Vector Calculus for Engineers
What is adaptive quadrature? Join me on Coursera: https://www.coursera.org/learn/numerical-methods-engineers Lecture notes at http://www.math.ust.hk/~machas/numerical-methods-for-engineers.pdf Subscribe to my channel: http://www.youtube.com/user/jchasnov?sub_confirmation=1
From playlist Numerical Methods for Engineers
Adaptive Sampling via Sequential Decision Making - András György
The workshop aims at bringing together researchers working on the theoretical foundations of learning, with an emphasis on methods at the intersection of statistics, probability and optimization. Lecture blurb Sampling algorithms are widely used in machine learning, and their success of
From playlist The Interplay between Statistics and Optimization in Learning
Adaptive Federated Optimization
A Google TechTalk, 2020/7/30, presented by Zachary Charles, Google ABSTRACT:
From playlist 2020 Google Workshop on Federated Learning and Analytics
Pandora's Box with Correlations: Learning and Approximation - Shuchi Chawla
Computer Science/Discrete Mathematics Seminar I Topic: Pandora's Box with Correlations: Learning and Approximation Speaker: Shuchi Chawla Affiliation: University of Wisconsin-Madison Date: April 05, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 11
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To follow along with the course, visit: http://cs330.stanford.edu/fall2021/index.html To view all online courses and programs offered by Stanford, visit: http:/
From playlist Stanford CS330: Deep Multi-Task & Meta Learning I Autumn 2021I Professor Chelsea Finn
Discrete Optimization Under Uncertainty - Sahil Singla
Short talks by postdoctoral members Topic: Discrete Optimization Under Uncertainty. Speaker: Sahil Singla Affiliation: Member, School of Mathematics Date: October 2, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
High-order Homogenization in Optimal Control by the Bloch Wave Method by Agnes Lamacz-Keymling
DISCUSSION MEETING Multi-Scale Analysis: Thematic Lectures and Meeting (MATHLEC-2021, ONLINE) ORGANIZERS: Patrizia Donato (University of Rouen Normandie, France), Antonio Gaudiello (Università degli Studi di Napoli Federico II, Italy), Editha Jose (University of the Philippines Los Baño
From playlist Multi-scale Analysis: Thematic Lectures And Meeting (MATHLEC-2021) (ONLINE)
Comparing Bayesian optimization with traditional sampling
Welcome to video #2 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video Sterling introduces Bayesian Optimization as an alternative method for sa
From playlist Optimization tutorial
Battery Optimization | Android App Development Tutorial For Beginners
🔥Post Graduate Program In Full Stack Web Development: https://www.simplilearn.com/pgp-full-stack-web-development-certification-training-course?utm_campaign=BatteryOptimization-ihtyTpOfbMc&utm_medium=Descriptionff&utm_source=youtube 🔥Caltech Coding Bootcamp (US Only): https://www.simplilea
From playlist Android App Development Tutorial Videos [Updated]
Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 7 - Kate Rakelly (UC Berkeley)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Kate Rakelly (UC Berkeley) Guest Lecture in Stanford CS330 http://cs330.stanford.edu/ 0:00 Introduction 0:17 Lecture outline 1:07 Recap: meta-reinforcement lear
From playlist Stanford CS330: Deep Multi-Task and Meta Learning
Stochastic Gradient Descent: where optimization meets machine learning- Rachel Ward
2022 Program for Women and Mathematics: The Mathematics of Machine Learning Topic: Stochastic Gradient Descent: where optimization meets machine learning Speaker: Rachel Ward Affiliation: University of Texas, Austin Date: May 26, 2022 Stochastic Gradient Descent (SGD) is the de facto op
From playlist Mathematics
Stochastic Tipping Points in Optimal Tumor Evasion and Adaptation Induced....by Jason George
PROGRAM TIPPING POINTS IN COMPLEX SYSTEMS (HYBRID) ORGANIZERS: Partha Sharathi Dutta (IIT Ropar, India), Vishwesha Guttal (IISc, India), Mohit Kumar Jolly (IISc, India) and Sudipta Kumar Sinha (IIT Ropar, India) DATE: 19 September 2022 to 30 September 2022 VENUE: Ramanujan Lecture Hall an
From playlist TIPPING POINTS IN COMPLEX SYSTEMS (HYBRID, 2022)
Fast By Default: Algorithmic Performance Optimization in Practice
We’ve learned to rely on sophisticated frameworks and fast engines so much that we’re slowly forgetting how computers work. With modern development tools, it’s easy to locate the exact code that’s slowing down your application, but what do you do next? Why exactly is it slow, and how do yo
From playlist Performance and Testing