Multidimensional Scaling by Particle Swarm Optimization
Explora el escalamiento multidimensional mediante optimización por enjambre de partículas con el experto Javier Trejos.
Metaheurísticas en Diseño Territorial: Mono-Objetivo y Bi-Objetivo
Explore metaheurísticas en diseño territorial, abordando enfoques mono-objetivo y bi-objetivo con la Dra. Bernábe-Loranca de la Universidad Autónoma de Puebla.
Curiosities and Counterexamples in Smooth Convex Optimization
Explore counterexamples and curiosities in smooth convex optimization, focusing on key concepts and unique aspects of this mathematical field.
Semidefinite Optimization Approaches for Reactive Optimal Power Flow Problems
Explore advanced optimization techniques for solving reactive optimal power flow problems in electrical power systems using semidefinite programming approaches.
Convex Optimization I - Fundamentals and Applications
Explore fundamental concepts and applications of convex optimization with Microsoft researcher Sebastien Bubeck in this comprehensive lecture on mathematical programming techniques.
Tensor Networks for Machine Learning and Applications
Explore tensor networks in machine learning, their benefits, and applications. Learn about model functions, optimization algorithms, and potential insights for matching architectures to data types.
Distributed Stochastic Non-Convex Optimization - Optimal Regimes and Tradeoffs
Explore optimal regimes and tradeoffs in distributed stochastic non-convex optimization with Usman A. Khan. Gain insights into advanced data science concepts and their applications in signal processing.
Delayed Column Generation in Large Scale Integer Optimization Problems
Explore delayed column generation for solving large-scale mixed integer programming problems, focusing on applications in data science and decision mathematics.
Randomized Linear Algebra for Interior Point Methods
Explore randomized linear algebra techniques for efficient interior point methods in linear programming, focusing on applications in data science and scientific computing.
Interior Point Methods
Explore advanced interior point methods for optimization, covering key ideas, path following analysis, and specialized algorithms like Renegar's and regularized John ellipse barrier.
Introduction to Interior Point Methods for Discrete Optimization, Lecture I
Explore interior point methods for optimization, their recent advancements, and applications in solving various continuous and combinatorial problems efficiently.
How to Read Math in Deep Learning Papers - A 5-Step Process
Learn a 5-step method to decipher mathematical formulas in deep learning papers, enhancing your understanding and intuition of complex AI concepts.
Compact Representations of Excited States from QMC, as Data for Low-Energy Models
Explore compact wave function representations for excited states in quantum mechanics, focusing on variational Monte Carlo methods and their applications in effective model development.
Geometry Optimization in Q-Chem - 2023 Virtual Winter School in Computational Chemistry
Overview of geometry optimization in Q-Chem, focusing on practical aspects of running calculations and paired with a hands-on lab exercise.
FTL Beam Optimizer - Building a Starship Weapon Trajectory Code - Episode 1
Explore beam weapon optimization in FTL game through coding, focusing on user interface, ship design, and room management techniques.
Stochastic Gradient Descent for Support Vector Machines - Lecture 22
Dive into stochastic gradient descent algorithms and their application in optimizing Support Vector Machines through practical implementation techniques and mathematical foundations.
Stochastic Gradient Descent for Support Vector Machines - Lecture 22A
Dive into stochastic sub-gradient descent optimization for Support Vector Machines and discover its connection to perceptron algorithms through practical implementation techniques.
Practical Issues with Neural Networks in Machine Learning - Lecture 27
Explore practical challenges and solutions for training large neural networks, including effective strategies and optimization techniques for improved performance.
No Train No Gain - Revisiting Efficient Training Algorithms for Transformer-based Language Models
Delve into efficient training algorithms for Transformer models, exploring dynamic architectures, batch selection, and optimizers while examining their effectiveness against baseline methods.
Discovering Black-Box Optimizers via Evolutionary Meta-Learning
Delve into evolutionary meta-learning techniques for discovering black-box optimizers, exploring advanced concepts in evolution strategies and genetic algorithms through self-attention architectures.
Open Source Vizier: Distributed Infrastructure for Blackbox Optimization and Hyperparameter Tuning
Explore Google's Vizier system for blackbox optimization and hyperparameter tuning, covering its distributed infrastructure, Python implementation, and integration with AutoML ecosystem.