31 citations · 73 across the 7 of their papers we have counts for
11 papers · 1 filter
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
Kenta Oono, Nontawat Charoenphakdee, Kotatsu Bito +14
Virtual Human Generative Model (VHGM) is a generative model that approximates the joint probability over more than 2000 human healthcare-related attributes. This paper presents the…
TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns
Soma Onishi, Kenta Oono, Kohei Hayashi
We present \emph{TabRet}, a pre-trainable Transformer-based model for tabular data. TabRet is designed to work on a downstream task that contains columns not seen in pre-training.…
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
Yuri Kinoshita, Kenta Oono, Kenji Fukumizu +2
Variational autoencoders (VAEs) are one of the deep generative models that have experienced enormous success over the past decades. However, in practice, they suffer from a problem…
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…
Fast Estimation Method for the Stability of Ensemble Feature Selectors
Rina Onda, Zhengyan Gao, Masaaki Kotera +1
It is preferred that feature selectors be \textit{stable} for better interpretabity and robust prediction. Ensembling is known to be effective for improving the stability of featur…
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…