Excited to share WaLa: our open-source, 1B-param 3D generative model. It supports diverse inputs: point clouds, voxels, images, text, depth, and multi-view. Offering rapid 3D shape generation in 2-4 seconds. Explore the code and pre-trained models:
@fchollet What’s your definition of symbolic? Or rather, when do you say a rule is not symbolic? I think compression always ends up in symbols that can present more than one thing. But some symbols are understandable and communicatable by humans, some aren’t. Don’t you think?
New #ICCV2025 paper: ✨ Aligning Constraint Generation with Design Intent in Parametric CAD ⚙️
We apply post-training techniques to the task of generating engineering sketch constraints found in parametric CAD, using a constraint solver for verifiable rewards.
@GaryMarcus@francoisfleuret Biological systems have a Markov Blanket. They model the world according to their Markov Blanket Boundary. LLMs model their input output distribution which is their MBB and what they can see of the world. Free Energy Principle. Right?
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@docmilanfar Well eq2 is very intuitive while I can’t even understand what the assumptions of eq1 are! 1/2PNr is simply the area of the triangle representing the integral of the interest over time assuming the principal is paid back uniformly.
I’m #hiring an AI Research Scientist summer Intern to work on 3D generation and editing. Location is flexible (remote/hybrid). Apply here: https://t.co/SmDv0z29yG Also, find many other interns and full-time AI positions @ Autodesk AI Lab in other exciting areas. #AI#3D
Excited to share WaLa: our open-source, 1B-param 3D generative model. It supports diverse inputs: point clouds, voxels, images, text, depth, and multi-view. Offering rapid 3D shape generation in 2-4 seconds. Explore the code and pre-trained models:
The problem @ylecun refers to, in this case, seems to be due to data bias and overfitting on a famous problem. As soon as you diverge from the original story it comes to its senses!