20 citations · 29 across the 2 of their papers we have counts for
7 papers
Universal approximation property of invertible neural networks
Isao Ishikawa, Takeshi Teshima, Koichi Tojo +3
Invertible neural networks (INNs) are neural network architectures with invertibility by design. Thanks to their invertibility and the tractability of Jacobian, INNs have various m…
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
Takeshi Teshima, Masashi Sugiyama
Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. When a CG is available, e.g., from the domain knowl…
Universal Approximation Property of Neural Ordinary Differential Equations
Takeshi Teshima, Koichi Tojo, Masahiro Ikeda +2
Neural ordinary differential equations (NODEs) is an invertible neural network architecture promising for its free-form Jacobian and the availability of a tractable Jacobian determ…
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
Takeshi Teshima, Isao Ishikawa, Koichi Tojo +3
Invertible neural networks based on coupling flows (CF-INNs) have various machine learning applications such as image synthesis and representation learning. However, their desirabl…
-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
Masahiro Fujisawa, Takeshi Teshima, Issei Sato +1
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications. However, ABC can be sensitive to outliers if a data dis…
Few-shot Domain Adaptation by Causal Mechanism Transfer
Takeshi Teshima, Issei Sato, Masashi Sugiyama
We study few-shot supervised domain adaptation (DA) for regression problems, where only a few labeled target domain data and many labeled source domain data are available. Many of…