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原文摘要

Input-convex neural nets parametrize the convex potentials of density models and transport maps, and their convexity requires the inter-layer weights to be non-negative. Projected gradient descent enforces this by projecting after each step, and due to mini-batch noise the boundary is re-crossed indefinitely, which leads to an active set the projection never identifies. The differentiable alternative, direct softplus, optimizes a free latent weight through a softplus positivity map. Where that latent weight is well below zero, the derivative of the map is exponentially small and scales down both the gradient and its noise. We call this region the shoulder, and a weight that reaches it therefore barely moves and stays for an exponentially long time. We propose the lift, which replaces the free latent weight by a learnable slack plus the output of a hypernetwork that reads a permutation-invariant summary of the training batch. The latent weight then varies with the same batch that forms the gradient, so the two are coupled. We show that this coupling adds a term to the variance of the update at first order, and that the slack, the batch dependence, and the shared batch are each needed for it. Where the coupling aligns positively with the loss curvature, a weight on the shoulder crosses the barriers of the loss sooner under the lift than under direct softplus, also under preconditioned stochastic gradient descent. We compare the lift with the two existing methods on several applications. Where a constrained weight of direct softplus reaches the shoulder, the lift fits the target more closely than direct softplus and reaches the same reconstruction about three times sooner. Where almost none reaches it, the methods agree.