Recent Progress on Grokking and Probabilistic Federated Learning
Explore recent advancements in grokking phenomenon and probabilistic federated learning. Gain insights into neural networks, Gaussian processes, and Bayesian inference for machine learning applications.
Probabilistic Programming Tutorial - Part 2
Explore advanced concepts in probabilistic programming, focusing on practical applications and techniques for scientific computing and data analysis.
Bayesian Tools for the Information Frontier at the LHC
Explore Bayesian machine learning tools and their applications in LHC particle physics research, focusing on information frontier analysis and scientific discovery methods.
Bayes Rule and Its Applications in Machine Learning - Lecture 5
Master Bayes' Rule fundamentals through practical examples and discover its crucial applications in machine learning for calculating model probabilities from data.
Probabilistic Machine Learning - Lecture 19
Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in this graduate-level academic session.
Probabilistic Machine Learning - Lecture 27
Dive into advanced data science concepts through comprehensive lecture coverage of key theoretical and practical aspects in modern data analysis and machine learning.
Probabilistic Methods for Data-Driven Reduced-Order Modeling
Explore probabilistic methods for data-driven reduced-order modeling, including Gaussian process regression, deep kernel learning, and Bayesian reduced-order operator inference for efficient multi-query simulations and digital twinning.
WEAVE: Building Complex HPC Workflows - Tutorials 2024
Explore WEAVE's integrated tools for building complex HPC workflows, enhancing productivity and scientific quality through common infrastructure and tool integration.
Advanced Techniques for Online Advertisement Spend Optimization
Explore advanced techniques for optimizing online ad spend using Reinforcement Learning, Bayesian statistics, and Thompson sampling to improve budget allocation and maximize returns.
Probabilistic Programming Tutorial - Part 1
Explore probabilistic programming fundamentals and applications with MIT expert Vikash Mansinghka in this comprehensive tutorial.
Statistical Analysis in Cosmology - Part 1
Explore Bayesian statistics, likelihood sampling, chi-square, and covariance matrix in cosmology. Learn parameter estimations in 2-point statistics and Markov Chain Monte Carlo.
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.
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.
Gaussian Processes
Explore Gaussian Processes with Daniel Hernández-Lobato, covering key concepts, applications, and advanced techniques in probabilistic machine learning.
Practical Parameter Identifiability and Handling of Censored Data with Bayesian Inference in Models of Tumour Growth
Explore Bayesian inference techniques for tumor growth modeling, focusing on parameter identification and censored data analysis in mathematical oncology research.
How to Benefit from Uncertainty: An Introduction to Paraconsistent Bayesian Update
Explore how paraconsistent and paracomplete logics enhance Bayesian reasoning, offering new approaches to handle contradictory evidence and uncertainty in scientific and AI applications.