Diffusion on syntax trees for program synthesis
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Diffusion on syntax trees for program synthesis
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Re: Diffusion on syntax trees for program synthesis
#2[flagged]
Re: Diffusion on syntax trees for program synthesis
#3[deleted]
Re: Diffusion on syntax trees for program synthesis
#4This is very cool! My first thought is: can this be applied to converting raster graphics to vector graphics (eg PNG to SVG)? Seems like a very similar problem, though probably much more computationally expensive.
Re: Diffusion on syntax trees for program synthesis
#5[flagged]
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Re: Diffusion on syntax trees for program synthesis
#6[dupe]
Re: Diffusion on syntax trees for program synthesis
#7It's funny, this kind of subtree mutation was looked at pretty deeply by Koza and Adamı in the 90s under the rubric of Genetic Algorithms, but with a slightly different optimization function
One ref in the paper to 2000 for GAs for fast generation of program trees, but that's missing the main show
Hope they're reading this and dig into those guys work
Re: Diffusion on syntax trees for program synthesis
#8This is very cool! My first thought is: can this be applied to converting raster graphics to vector graphics (eg PNG to SVG)? Seems like a very similar problem, though probably much more computationally expensive.
> We apply our approach to inverse graphics tasks, where our model learns to convert images into programs that produce those images.
I would argue that at least on a philosophical level, this is, definitionally, the process of converting raster graphics to vector graphics, as long as you by the premise that the difference between the two is simply that vector gfx is a programmatic/imperative representation of image generation, while raster is a data structure/declarative representation of images.
In other words, simply put, raster images are just arrays, vector images are sequences of instructions. Raster images are naturally much closer to the “raw” data needed by the output mechanism, while vector images require a much more complex interpreter.
Re: Diffusion on syntax trees for program synthesis
#9This is very cool! My first thought is: can this be applied to converting raster graphics to vector graphics (eg PNG to SVG)? Seems like a very similar problem, though probably much more computationally expensive.
> We apply our approach to inverse graphics tasks, where our model learns to convert images into programs that produce those images. I would argue that at least on a philosophical level, this is, definitionally, the process of converting raster graphics to vector graphics, as long as you by the premise that the difference between the two is simply that vector gfx is a programmatic/imperative representation of image g…
Or, raster and vector images are philosophically the same thing. The only difference is that vector has more operations than raster. Raster just has "draw unit square at integer coordinates".
Re: Diffusion on syntax trees for program synthesis
#10It's funny, this kind of subtree mutation was looked at pretty deeply by Koza and Adamı in the 90s under the rubric of Genetic Algorithms, but with a slightly different optimization function One ref in the paper to 2000 for GAs for fast generation of program trees, but that's missing the main show Hope they're reading this and dig into those guys work
You can also say backpropagation is the chain rule from centuries ago.