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A method to construct exponential families by representation theory
In this paper, we give a method to construct "good" exponential families systematically by representation theory.
On a method to construct exponential families by representation theory
In arXiv:1811. 01394, we introduced a method to construct an exponential family $\mathcal{P}=\{p_\theta\}_{\theta\in\Theta}$ on a homogeneous space $G/H$ from a pair $(V, v_0)$.
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
We answer this question by showing a convenient criterion: a CF-INN is universal if its layers contain affine coupling and invertible linear functions as special cases.
Universal Approximation Property of Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs) is an invertible neural network architecture promising for its free-form Jacobian and the availability of a tractable Jacobian determinant estimator.
Universal approximation property of invertible neural networks
Invertible neural networks (INNs) are neural network architectures with invertibility by design.
