A new differentiable layer with convex parameter space universally approximates generalized convex functions and their gradients, enabling single-level reformulations of bilevel problems in optimal transport and multi-good auctions.
Jacnet: Learning functions with structured jacobians
2 Pith papers cite this work. Polarity classification is still indexing.
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JacobianODE learns Jacobians from data to quantify directional control in nonlinear systems and shows sensory-to-cognitive control strengthening in a trained working-memory RNN.
citing papers explorer
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Universal Representation of Generalized Convex Functions and their Gradients
A new differentiable layer with convex parameter space universally approximates generalized convex functions and their gradients, enabling single-level reformulations of bilevel problems in optimal transport and multi-good auctions.
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Characterizing control between interacting subsystems with deep Jacobian estimation
JacobianODE learns Jacobians from data to quantify directional control in nonlinear systems and shows sensory-to-cognitive control strengthening in a trained working-memory RNN.