A lift for input-convex neural network training

In three linesNovel training method for input-convex neural networks (ICNNs) using an unconstrained hypernetwork that emits inter-layer weights. Approach inspired by parameter-extension lifts from PDE-constrained inverse problems, circumvents limitations of projected gradient descent and softplus reparametrization. Results on log-concave density estimation and convex-potential normalizing flows show improved convergence.

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