Continuous deep learning architectures enable learning of flexible probabilistic modelsforpredictivemodeling asneuralordinary differential equations (ODEs), and for generative modeling as continuous normalizing flows.

Inthis experiment, we only quantize the weights, not the activations, to compare the performance degradation as weight bit-width decreases. The mean squared errors (MSE) of the weights across different bit-widths are also reported. The name of the layer and the number of parameters in parenthesis are shown in the column. All numbers are results of the last epoch. Table A3: ResNet-32 trained with Adam on the CIFAR-100 dataset.

The mistakebounds we prove are of the form O(Dγ2). The term 1γ2 is analogous to the usual margin term in SVM (perceptron) bounds.

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