Neural Scaling Laws - Fitting Scaling Laws for Scientific Foundation Models - Tutorial 3
Explore neural scaling laws and learn techniques for fitting scaling laws to scientific foundation models in this advanced tutorial.
Discovery in High Dimensions - 2008
Explore statistical methods for uncovering hidden structures in high-dimensional data from various fields, including genetics, astronomy, and consumer behavior.
Nematic Liquid Crystals: Where Mathematics Meets Physics and Applications
Explore the fascinating intersection of mathematics, physics, and applications in nematic liquid crystals with Prof. Apala Majumdar's insightful lecture.
Self-Avoiding Walk, Spin Systems, and Renormalisation - Clay Math Public Lecture
Explore self-avoiding walks, spin systems, and renormalisation in mathematics with Professor Gordon Slade. Gain insights into stochastic systems for anomalous diffusion in this Clay Math Public Lecture.
Introduction to Artificial Intelligence - Bandit Algorithms - Lecture 14
Explore bandit algorithms in AI, covering key concepts and applications in problem-solving and decision-making scenarios. Learn techniques for balancing exploration and exploitation.
Introduction to Artificial Intelligence - Bandit Algorithms - Lecture 15
Explore bandit algorithms in AI, learning key concepts and applications for modern problem-solving in gaming environments.
Intro to Artificial Intelligence - RL Monte Carlo Methods - Lecture 18
Explore Monte Carlo methods in reinforcement learning, applying algorithmic techniques to solve AI problems in gaming environments.
Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 3
Explorez les dynamiques d'optimisation en très haute dimension et leur application en apprentissage automatique, en mettant l'accent sur les matrices aléatoires et les statistiques résumées.
Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 4
Explore random matrices and optimization dynamics in high dimensions, focusing on effective dynamics, spectral transitions, and applications in machine learning algorithms.
Open Problems in European Artificial Intelligence
Explore open challenges in European AI with Prof. Schölkopf, delving into cutting-edge research and future directions in the field.
Building Normalizing Flows with Stochastic Interpolants
Explore advanced techniques for constructing normalizing flows using stochastic interpolants, enhancing generative modeling capabilities.
Reflected Diffusion Models
Explore innovative techniques for enhancing diffusion models, improving sample quality and efficiency in generative AI applications.
Action Matching: Learning Stochastic Dynamics from Samples
Learn stochastic dynamics from samples using action matching techniques. Explore innovative approaches to understanding and modeling complex systems through data-driven methods.
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
Explore discrete diffusion modeling techniques for estimating data distribution ratios, enhancing generative AI capabilities and understanding.
Generative Sequence Models for Sequential Decision Making
Explore generative sequence models for decision-making under uncertainty. Learn how Transformer architecture and language modeling advances can enhance offline learning, online exploration, and multi-task generalization in AI.
Learning and Amortized Inference in Probabilistic Programs
Explore probabilistic programming, amortized inference, and novel learning methods like reweighted wake-sleep and thermodynamic variational objective for efficient Bayesian inference in complex models.
Deep Generative Models - Lectures 2 and 3
Explore advanced deep generative models with expert insights on latest techniques, applications, and theoretical foundations in probabilistic AI.
Probabilistic Modeling and Programming Tutorial - Lecture 1
Explore probabilistic modeling and programming techniques with expert guidance, enhancing your AI and machine learning skills through hands-on tutorials and practical examples.
Diffusion Models
Explore advanced concepts in diffusion models, including theory, applications, and recent developments in probabilistic AI.
Quantum-Circuit Models for Many-Body Physics Out of Equilibrium - Lecture 1
Explore quantum-circuit models for non-equilibrium many-body physics, covering quantum cellular automata, local Hamiltonian dynamics, and locality-preserving unitaries.
Incompressibility of Random Walks on Unitary Group
Explore the mathematical intricacies of random walks on unitary groups, delving into their incompressibility properties and implications for quantum information theory.