Explore the second part of a comprehensive lecture on Deep Ontological Networks, focusing on the reasoning process. Delve into advanced concepts presented by Professor Gerardo Simari from UNS, Argentina. Learn about Datalog Ontologies, the RRN (Relational Reasoning Network) model, its learning and prediction processes, and Gated Recurrent Units (GRUs). Examine detailed algorithms for generating individual embeddings and RRN training. Access accompanying slides and a published paper in JAIR for further study. Gain valuable insights into this cutting-edge area of artificial intelligence, combining symbolic methods and deep learning, as part of the Neuro Symbolic Channel's content derived from Arizona State University's AI course.
Deep Ontological Networks: Reasoning Process - Part 2
Overview
Syllabus
Intro
Towards a Problem Statement
Datalog Ontologies: Example
RRN Model: Intuitions
RRN Learning: Intuitions
RRN Learning: Overview
RRN Prediction: Intuition
A Deeper Dive: Setup
A Deeper Dive: Model
Gated Recurrent Units (GRUS)
Algorithm 1: Generating individual embeddings
A Deeper Dive: Prediction
A Deeper Dive: Training
Algorithm 2: RRN Training
Taught by
Neuro Symbolic
