Takeshi Teshima | DeepAI

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∙ 04/15/2022

Universal approximation property of invertible neural networks

Invertible neural networks (INNs) are neural network architectures with ...

0 Isao Ishikawa, et al. ∙

∙ 12/19/2021

Rethinking Importance Weighting for Transfer Learning

A key assumption in supervised learning is that training and test data f...

0 Nan Lu, et al. ∙

∙ 02/27/2021

Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation

Causal graphs (CGs) are compact representations of the knowledge of the ...

0 Takeshi Teshima, et al. ∙

∙ 12/04/2020

Universal Approximation Property of Neural Ordinary Differential Equations

Neural ordinary differential equations (NODEs) is an invertible neural n...

0 Takeshi Teshima, et al. ∙

∙ 06/20/2020

Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators

Invertible neural networks based on coupling flows (CF-INNs) have variou...

0 Takeshi Teshima, et al. ∙

∙ 06/13/2020

γ-ABC: Outlier-Robust Approximate Bayesian Computation based on Robust Divergence Estimator

Making a reliable inference in complex models is an essential issue in s...

0 Masahiro Fujisawa, et al. ∙

∙ 06/12/2020

Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation

The estimation of the ratio of two probability densities has garnered at...

0 Masahiro Kato, et al. ∙

∙ 02/10/2020

Few-shot Domain Adaptation by Causal Mechanism Transfer

We study few-shot supervised domain adaptation (DA) for regression probl...

3 Takeshi Teshima, et al. ∙

∙ 09/13/2018

Clipped Matrix Completion: a Remedy for Ceiling Effects

We consider the recovery of a low-rank matrix from its clipped observati...

0 Takeshi Teshima, et al. ∙

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