Probabilistic Machine Learning in Bioinformatics
Explore probabilistic machine learning applications in bioinformatics, focusing on cutting-edge techniques and their impact on biological data analysis.
What's New with Turing.jl and Its Ecosystem
Explore updates to Turing.jl, a probabilistic programming framework in Julia for Bayesian inference, and its expanding ecosystem of modular packages for enhanced usability and extensibility.
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.
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.
Probabilistic Programming Tutorial - Part 1
Explore probabilistic programming fundamentals and applications with MIT expert Vikash Mansinghka in this comprehensive tutorial.
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.