Graph Theory Courses

Simons Semester on Dynamics

Equilibrium States Through Potential Theory - Lecture 1

Dive into equilibrium states and their role in thermodynamic formalism, exploring their construction for rational maps and endomorphisms using (pluri)potential theory.

• YouTube
• 1 hour 39 minutes
• Self-Paced
• Free Video

Simons Semester on Dynamics

Equilibrium States Through Potential Theory - Lecture 2

Dive into equilibrium states for rational maps and endomorphisms, exploring their construction through potential theory and their statistical properties in thermodynamic formalism.

• YouTube
• 1 hour 32 minutes
• Self-Paced
• Free Video

Up and Atom

Maxwell's Equations - A Beginner's Guide to Electromagnetic Theory

Discover the fundamental principles of electromagnetism through Maxwell's four equations, exploring their mathematical foundations and real-world applications in modern physics.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Machine Translation: Automata Theory, Probability, and Linguistics - 2007

Explore machine translation's evolution through automata theory, probability, and linguistics. Delve into tree-based models and their impact on natural language processing.

• YouTube
• 1 hour 4 minutes
• Self-Paced
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Can Graph Neural Networks Help Logic Inference? - 2019

Explore how graph neural networks can bridge perceptual learning and logic inference, potentially advancing AI. Examine evidence from inductive logic programming and lifted logic inference.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Information and Decision Theory Explain Neural Pulse Trains - Lecture

Explore information and decision theory's application to neural communication, uncovering insights into pulse trains and channel capacity in the peripheral nervous system.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

AI Institute at UofSC - #AIISC

BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks

Explore brain network analysis using Graph Neural Networks with BrainGB, a comprehensive benchmark for mapping human brain connectivity through structural and functional neuroimaging.

• YouTube
• 28 minutes
• Self-Paced
• Free Video

Valence Labs

A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks

Explore advanced techniques for building efficient 3D equivariant graph neural networks, focusing on local substructure encoding and frame transition encoding for improved expressiveness and performance.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Valence Labs

Graph Neural Networks for Link Prediction with Subgraph Sketching

Explore advanced Graph Neural Networks for link prediction, focusing on subgraph sketching techniques to improve expressiveness and efficiency in large-scale graph applications.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Valence Labs

DGL 1.0 - Empowering Graph Machine Learning for Everyone

Explore Deep Graph Library 1.0's comprehensive support for Graph Machine Learning, featuring DGL Sparse for advanced models and scalability improvements for real-world applications.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

Valence Labs

Interpretable Chirality-Aware Graph Neural Networks for QSAR Modeling in Drug Discovery

Explore an innovative Graph Neural Network approach for drug discovery, incorporating molecular chirality and offering interpretable results to enhance quantitative structure-activity relationship modeling.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Valence Labs

Likelihood Training of Schrödinger Bridge Using Forward-Backward SDEs Theory

Explore likelihood training of Schrödinger Bridge using Forward-Backward SDEs theory for deep generative modeling, offering mathematical flexibility and optimization principles.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

Valence Labs

On the Expressive Power of Geometric Graph Neural Networks

Explore the expressive power of geometric graph neural networks, focusing on the Geometric Weisfeiler-Leman test and its implications for distinguishing geometric graphs while respecting physical symmetries.

• YouTube
• 1 hour 48 minutes
• Self-Paced
• Free Video

Valence Labs

Modeling Interactions Between Vertices in Graph Neural Networks

Explore Graph Neural Networks' ability to model vertex interactions through theoretical analysis and practical applications, including a novel edge sparsification algorithm for improved GNN performance.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Video

Valence Labs

DiGress: Discrete Denoising Diffusion for Graph Generation

Explore DiGress, a discrete denoising diffusion model for graph generation with categorical attributes. Learn about its innovative approach, challenges, and state-of-the-art performance in molecular and non-molecular datasets.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video