Deductive Reasoning for Cross-Knowledge Graph Entailment - Part 1

Explore deductive reasoning for cross-knowledge graph entailment in this 17-minute video from the Neuro Symbolic Channel. Delve into the framework of deduction, knowledge graphs, and memory networks as part of a series on cutting-edge artificial intelligence and machine learning concepts. Learn about inference types, embedding methods, memory network structures, and their training processes. Examine the problem formulation, syntactic normalization, and model architecture for cross-knowledge graph entailment. Access accompanying slides and research paper for deeper understanding. Originally from an AI course at Arizona State University, this presentation by Aniruddha Datta, Ethan Clark, and Viswa Ivaturi offers valuable insights into the intersection of symbolic methods and deep learning.

Intro
Description and Types of Inference
Description and Uses
Methods of Embedding
Structure of Memory Networks
Inference of Memory Networks
Training/Learning in Memory Networks
Problem Formulation
Syntactic Normalization
Model Architecture
Model Description
Cross KG entailment