Alignment in deep networks is governed by flag varieties, with subspace intersection dimension as the unique reparameterization-invariant observable, explaining regularization and activation effects from first principles.
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Aligning the DDIM forward diffusion process with flow-matching manifold evolution enables high-quality generation without time conditioning, and class-conditional synthesis is possible with an unconditional denoiser by using separate time spaces per class.
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Flag Varieties: A Geometric Framework for Deep Network Alignment
Alignment in deep networks is governed by flag varieties, with subspace intersection dimension as the unique reparameterization-invariant observable, explaining regularization and activation effects from first principles.
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Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds
Aligning the DDIM forward diffusion process with flow-matching manifold evolution enables high-quality generation without time conditioning, and class-conditional synthesis is possible with an unconditional denoiser by using separate time spaces per class.