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It is not what a deep net can approximate that matters, but how it learns to approximate. Matrix perturbation theory explains why the loss surface of a ResNet or DenseNet is less erratic and eccentric than a ConvNet and hence easier to optimize under SGD arxiv.org/abs/2203.03099

Does recursive training of generative models only lead to a loss of diversity or is there more? We show that 'self-consuming' generative models don't only face model collapse, they can emphasize hidden artifacts of the generative model as well! arxiv.org/abs/2307.01850 🧵(1/N)

Engineering is art informed by science.