[x3d-public] Geometric/Geographic Deep Learning

[John Carlson]

We are speaking of the difference between a HyperMovie and a HyperShape, at a fundamental level.  One is appearance, the other is geometry.  What is the appropriate type of neural network for handling geometry?

Yes, I realize it’s pixels when you look at it. However, a shape is not always rectangular or flat.   This is applying neural networks to Non-Euclidean data (or so they say).

Seems like a very complex problem how to feed various geometries to a neural network. I would tend toward rectangularizing it.

So similar to how a CNN traverses through an image finding features, a GCN or HCN traverses through a graph or hypergraph finding features.   That’s the main difference.  The similarity is that they’re all convolutional.

I agree that images can be graphs and graphs can be images.

Hmm.

John

Sent from Mail for Windows 10

From: John Carlson
Sent: Wednesday, May 22, 2019 11:02 AM
To: Joseph D Williams; X3D Graphics public mailing list
Subject: RE: [x3d-public] Geometric/Geographic Deep Learning

So instead of dealing with output from neural networks being voxels, maybe, just maybe, we can have graphs and meshes?  I’m not entirely clear on the distinction between the data and the network. I guess a GCN can take graphs as input?

https://arxiv.org/pdf/1903.10384.pdf


John

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From: John Carlson
Sent: Wednesday, May 22, 2019 10:44 AM
To: Joseph D Williams; X3D Graphics public mailing list
Subject: RE: [x3d-public] Geometric/Geographic Deep Learning

No, you don’t get it.  It’s not even a picture/image/frame.  It’s a graph/mesh.  Not a CNN.   A GCN or HCN.  No one said anything about moving or frames except you.

In other words, we’ve gotten past pixels in our thinking.

John

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From: Joseph D Williams
Sent: Wednesday, May 22, 2019 10:36 AM
To: John Carlson; X3D Graphics public mailing list
Subject: RE: [x3d-public] Geometric/Geographic Deep Learning

https://www.youtube.com/watch?v=aircAruvnKk

When things are moving, we can start to think of frames. If no movement, only one frame needed. 
Well, we start with the idea of a network, then thinking about how to invent a computing structure to compute the stuff.
The hardware and the training seems to be very important. That phrase of continuing integration holds the idea of a dynamically changing output result. 
Joe



From: John Carlson
Sent: Sunday, May 19, 2019 12:18 PM
To: Joseph D Williams; X3D Graphics public mailing list
Subject: RE: [x3d-public] Geometric/Geographic Deep Learning

Uh, I just wanted to do geometric and geographic deep learning?

“Frame”?  https://www.youtube.com/watch?v=D3fnGG7cdjY
John

Sent from Mail for Windows 10

From: Joseph D Williams
Sent: Sunday, May 19, 2019 11:58 AM
To: John Carlson; X3D Graphics public mailing list
Subject: RE: [x3d-public] Geometric/Geographic Deep Learning


Anything you wish to discuss involving anticipation, simulation, recognition, labeling, intentionality, inclusion, exclusion, semantic and physical relationships, what the computer wants to see, deep learning, and continuous integration, then watch some of this.

https://www.youtube.com/watch?v=-b041NXGPZ8

to convolve and deconvolve is basic. How many frames you want? How many neurons you got? 

Thanks, 
Joe


From: John Carlson
Sent: Sunday, May 19, 2019 8:34 AM
To: X3D Graphics public mailing list
Subject: [x3d-public] Geometric/Geographic Deep Learning

Finally, something that interests me about deep learning!  Is anyone working on geometric or geographic deep learning?  It appears like these subfields of deep learning have emerged, based on Graph Convolution Networks (GCNs), and perhaps HyperGCNs.

Thanks,

John







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