Functional gradient descent with adaptive representations converges to stationary points or global minimizers despite approximation errors and outperforms fixed approximations and neural network baselines.
AsymFlow restricts noise prediction to a low-rank subspace to recover full-dimensional velocity, achieving 1.57 FID on ImageNet and enabling latent-to-pixel flow finetuning.