YoVDO

Probabilistic Inference Using Contraction of Tensor Networks

Offered By: The Julia Programming Language via YouTube

Tags

Probabilistic Inference Courses Artificial Intelligence Courses Machine Learning Courses Probabilistic Graphical Models Courses Bayesian Networks Courses Tensor Networks Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore probabilistic inference using tensor network contraction in this 29-minute conference talk from JuliaCon 2024. Dive into the world of reasoning under uncertainty and learn how TensorInference.jl, a Julia package, combines probabilistic graphical models (PGMs) with tensor networks to enhance performance in complex probabilistic inference tasks. Discover the challenges of exact and approximate inference methods, and understand how tensor networks offer a powerful solution for representing complex system states. Gain insights into optimizing contraction sequences, leveraging differentiable programming, and utilizing advanced contraction methods like TreeSA, SABipartite, KaHyParBipartite, and GreedyMethod. Learn about the package's support for generic element types, hyper-optimized contraction order settings, and integration with BLAS routines and GPU technology for improved efficiency. Explore applications in AI, medical diagnosis, computer vision, and natural language processing while understanding the potential of exact methods in probabilistic inference.

Syllabus

Probabilistic inference using contraction of tensor networks | Roa-Villescas | JuliaCon 2024


Taught by

The Julia Programming Language

Related Courses

Knowledge and Uncertainty
Brilliant
Time, Change, and Decisions for Marketing
University of Colorado System via Coursera
Graphical Models Certification Training
Edureka
Introduction to Artificial Intelligence
Independent
Machine Learning and AI Foundations: Causal Inference and Modeling
LinkedIn Learning