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05/23/2022
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization
In this paper, we propose a strategy to construct a multi-objective opti...
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10/15/2021
Equivariant and Invariant Reynolds Networks
Invariant and equivariant networks are useful in learning data with symm...
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04/10/2021
Approximate Bayesian Computation of Bézier Simplices
Bézier simplex fitting algorithms have been recently proposed to approxi...
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02/17/2021
Group Equivariant Conditional Neural Processes
We present the group equivariant conditional neural process (EquivCNP), ...
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12/27/2020
Universal Approximation Theorem for Equivariant Maps by Group CNNs
Group symmetry is inherent in a wide variety of data distributions. Data...
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10/23/2020
On the Number of Linear Functions Composing Deep Neural Network: Towards a Refined Definition of Neural Networks Complexity
The classical approach to measure the expressive power of deep neural ne...
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10/15/2019
Improved Generalization Bound of Permutation Invariant Deep Neural Networks
We theoretically prove that a permutation invariant property of deep neu...
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06/17/2019
Asymptotic Risk of Bezier Simplex Fitting
The Bezier simplex fitting is a novel data modeling technique which expl...
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03/05/2019
Universal approximations of permutation invariant/equivariant functions by deep neural networks
In this paper,we develop a theory of the relationship between permutatio...
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05/18/2018
