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GNN-based human awareness for navigation
⏲ 34 sec ✓ 09-May-2019
An Introduction to Graph Neural Networks: Models and Applications
⏲ 59 minutes 👁 133.8K
Graph Neural Networks - a perspective from the ground up
⏲ 14 minutes 28 seconds 👁 67K
Accelerating Healthcare AI: Enabling GNN in Graph Autoencoder Techniques and Making AI Teams More Successful
⏲ 8 min 76 sec ✓ 19-Feb-2022
The ultimate intro to Graph Neural Networks. Maybe.
⏲ 8 minutes 35 seconds 👁 38.9K
Think Graph Neural Networks (GNN) are hard to understand? Try this two part series..
⏲ 1 hour 54 minutes 59 seconds 👁 11.6K
SILO: Claire Donnat
⏲ 42 min 17 sec ✓ 29-Dec-2022
But what is a convolution?
⏲ 23 minutes 1 second 👁 1.2M
Do you want to know Graph Neural Networks (GNN) implementation in Python?
⏲ 1 hour 59 minutes 6 seconds 👁 9.8K
GEOMETRIC DEEP LEARNING BLUEPRINT
⏲ 3 hours 33 minutes 23 seconds 👁 81.4K
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 6.2 - Basics of Deep Learning
⏲ 29 minutes 31 seconds 👁 20.2K
CODE: GRAPH Link Prediction wDGL on Pytorch and PyGCode Example | GraphML | GNN
⏲ 21 minutes 16 seconds 👁 5.3K
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_gnn-based-human-awareness-for-navigation.webp "This video shows a proof of concept software were we use machine learning to estimate the degree of social compliance of the robot when navigating.")
_an-introduction-to-graph-neural-networks-models-and-applications.webp "Microsoft Research")
_graph-neural-networks-a-perspective-from-the-ground-up.webp "Alex Foo")
_accelerating-healthcare-ai-enabling-gnn-in-graph-autoencoder-techniques-and-making-ai-teams-more-successful.webp "Tom Wilson, VP Vertical Markets, GraphcorennAbstractnAcross a wide range of use cases in Healthcare AI there is actually a short list of emerging ML approaches and model architectures under investigation and early deployment. These approaches typically involve autoencoders, and especially generative autoencoders, to make maximal use of diverse data sets. The autoencoders encode the available data space into a latent space, or embedding space, with which machine learning approaches can accomplish")
_the-ultimate-intro-to-graph-neural-networks-maybe.webp "AI Coffee Break with Letitia")
_think-graph-neural-networks-gnn-are-hard-to-understand-try-this-two-part-series.webp "650 AI Lab")
_silo-claire-donnat.webp "Date: 12/14/22nnTitle: Inference using Network Structure: A comparison of statistical and machine learning perspectivesnnAbstract: Recent years have witnessed a surge in the amount of available structured data, typically modeled as a network capturing relationships (e.g. similarities, interactions) between different entities. In a typical data analysis setting, this network structure arises either “horizontally” across features, or “vertically” across observations. A large body of litera")
_but-what-is-a-convolution.webp "3Blue1Brown")
_do-you-want-to-know-graph-neural-networks-gnn-implementation-in-python.webp "650 AI Lab")
_geometric-deep-learning-blueprint.webp "Machine Learning Street Talk")
_stanford-cs224w-machine-learning-with-graphs-124-2021-124-lecture-6-2-basics-of-deep-learning.webp "Stanford Online")
_code-graph-link-prediction-w-dgl-on-pytorch-and-pyg-code-example-124-graphml-124-gnn.webp "code_your_own_AI")
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