Factorizable Normalizing Flows represent parameter-dependent densities via a reference flow composed with a factorized polynomial transformation, enabling isolated per-parameter learning and linear scaling.
Input Convex Neural Networks
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abstract
This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that the output of the network is a convex function of (some of) the inputs. The networks allow for efficient inference via optimization over some inputs to the network given others, and can be applied to settings including structured prediction, data imputation, reinforcement learning, and others. In this paper we lay the basic groundwork for these models, proposing methods for inference, optimization and learning, and analyze their representational power. We show that many existing neural network architectures can be made input-convex with a minor modification, and develop specialized optimization algorithms tailored to this setting. Finally, we highlight the performance of the methods on multi-label prediction, image completion, and reinforcement learning problems, where we show improvement over the existing state of the art in many cases.
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2026 4verdicts
UNVERDICTED 4representative citing papers
A convex neural network is trained inside an elastoplastic stress integration loop using force equilibrium losses to identify yield functions from full-field displacement data.
New polyconvex PANN constitutive models for anisotropic hyperelasticity are introduced along with the first polyconvex integrity basis for tetragonal symmetry and functional basis for cubic symmetry, both obtained through group symmetrization of triclinic invariants.
PINN-AFE uses multi-head attention and input convex networks to solve Monge-Ampère equations with claimed accuracy, efficiency, and extensions to image enhancement and medical registration.
citing papers explorer
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Advances in polyconvex anisotropic hyperelasticity
New polyconvex PANN constitutive models for anisotropic hyperelasticity are introduced along with the first polyconvex integrity basis for tetragonal symmetry and functional basis for cubic symmetry, both obtained through group symmetrization of triclinic invariants.