YoVDO

How to Represent Part-Whole Hierarchies in a Neural Network - Geoff Hinton's Paper Explained

Offered By: Yannic Kilcher via YouTube

Tags

Neural Networks Courses Capsule Networks Courses Object Recognition Courses Contrastive Learning Courses

Course Description

Overview

Explore Geoffrey Hinton's GLOM model for computer vision in this comprehensive video explanation. Dive into the innovative approach that combines transformers, neural fields, contrastive learning, capsule networks, denoising autoencoders, and RNNs to dynamically construct parse trees for object recognition. Learn about the multi-step consensus algorithm, cross-column attention mechanism, and how GLOM handles video input. Discover the potential of this new AI approach for visual scene understanding, including discussions on architecture, training methods, and design decisions.

Syllabus

- Intro & Overview
- Object Recognition as Parse Trees
- Capsule Networks
- GLOM Architecture Overview
- Top-Down and Bottom-Up communication
- Emergence of Islands
- Cross-Column Attention Mechanism
- My Improvements for the Attention Mechanism
- Some Design Decisions
- Training GLOM as a Denoising Autoencoder & Contrastive Learning
- Coordinate Transformations & Representing Uncertainty
- How GLOM handles Video
- Conclusion & Comments


Taught by

Yannic Kilcher

Related Courses

Deep Learning For Visual Computing
Indian Institute of Technology, Kharagpur via Swayam
Using GPUs to Scale and Speed-up Deep Learning
IBM via edX
Introduction To Brain & Behaviour
Indian Institute of Technology Kanpur via Swayam
Computer Vision
Nvidia Deep Learning Institute via Udacity
TensorFlow for CNNs: Object Recognition
Coursera Project Network via Coursera