Fully Input Convex CNNs

The function buildConvexCNN adds the capability to build fully input convex convolutional neural networks. The function trainConstrainedNetwork has been updated to facilitate training these networks.

More Flexible Convex MLPs

1. The convex architecture produced by the buildConstrainedNetwork function has been modified. The positivity constraint on the weights of the skip connections has been removed, as it was unnecessary for maintaining convexity.

2. The PositiveNonDecreasingActivationFunction name-value argument in the buildConstrainedNetwork function has been renamed to ConvexNonDecreasingActivation.

Initial Release

This initial release of the Constrained Deep Learning repository includes examples that demonstrate how to design and train multi-layer perceptions (MLPs) under the following constraints:

1. Convexity
2. Monotonicity
3. Lipschitz Continuity

For further details, please refer to the README.