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Doesn’t minimizing the L1 norm correspond to performing MLE with laplacian errors?

Do you know whether this will be cancelled given the FTX situation?

If the optimal norm is below the minimum you can achieve just by re-scaling, you are trading-off training set accuracy for weights with a smaller norm within each layer. It's not that weird that the best known way of making this trade-off is by constrained optimization.

And a 3D one by optimizing a differentiable volumetric representation using 2D diffusion:

Emad from Stability AI (the people behind Stable Diffusion) says that they will make a model better than this.

Answer by P.Sep 28, 202220

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