[x3d-public] Geometric/Geographic Deep Learning
Joseph D Williams
joedwil at earthlink.net
Wed May 22 12:55:23 PDT 2019
➢ What is the appropriate type of neural network for handling geometry?
In the simple example of recognizing a 2d shape, like a letter, the decision network appears as a set of connected vertices where, for example the first level has an element that only turns on when something parses out a sequence of connected real of virtual connected basic elements that form for example, a part of a curve. Then, another element that might turn on for another part of a shape. The next decision level is somehow trained so that one of its elements turns on when it sees that one or more of the elements above it turns on, maybe when enough of the first ones are seeing enough of an arc to make a circle. Downstream, another level is trained to see that a combination of a circle and other features make a character. I see it as a form of cascade where the state of multiple set(s) of inputs produce some known reaction(s) from the network.
That is why that tesla video says a lot me, because they went ahead and, as far as we know, produced the first computing hardware designed for this type of neural network control decision making, at least for open commerce, at this scale, not as a kit.
Now when you get multiple inputs, even if only looking at the same thing with different viewpoints and lighting, then another level of decision making is learning how to put the various learnable elements together.
So, for nD stuffs, to me, first it is getting whatever it is rendered so it can be analyzed and manipulated as the author intended. Now please start to look for various nD artifacts that can be used to semantically represent basic nD elements that can generally and specifically represent basic elements in the universe you are interested in.
So in the hardware, it is the matter of having enough trainable connections to produce a competent result.
Hardware operations and external interfaces use some sort of programming that can be trained to learn how to operate the hardware and learn how to learn from system inputs and outputs.
➢ feed various geometries to a neural network
First you get the scene in front of you and the learning system in the form that the author intended, then you train the trainer to recognize whatever it is you want to put into it or get out of it. That is why I still believe that if you have enough points and triangles you can produce a smooth or irregular shape given whatever the rendering concept. And, I am confident that a network can easily be trained to learn about depth.
The tesla autonomous navigation video tried to emphasize that the training can be from simulated data or from real data, but how much better the real stuff is than the simulated stuff. The need is to advance with accumulation of data and learning enough about the data and then derive those recognizable representational aspects that provide whatever assistance you need. As expected, given the current state of the art and the final user, the dependence on the completeness of the data, the competence of the initial and ongoing trainers, and the ability of the system to learn how to effectively simulate itself using novel stimulations and responses while allowing continuous integration. .
https://www.youtube.com/watch?v=IHZwWFHWa-w
Joe
From: John Carlson
Sent: Wednesday, May 22, 2019 9:50 AM
To: Joseph D Williams; X3D Graphics public mailing list
Subject: RE: [x3d-public] Geometric/Geographic Deep Learning
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
Sent from Mail for Windows 10
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
Sent from Mail for Windows 10
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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