49 citations · 93 across the 7 of their papers we have counts for
7 papers
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…
When is invariance useful in an Out-of-Distribution Generalization problem ?
Masanori Koyama, Shoichiro Yamaguchi
The goal of Out-of-Distribution (OOD) generalization problem is to train a predictor that generalizes on all environments. Popular approaches in this field use the hypothesis that…
MANGA: Method Agnostic Neural-policy Generalization and Adaptation
Homanga Bharadhwaj, Shoichiro Yamaguchi, Shin-ichi Maeda
In this paper we target the problem of transferring policies across multiple environments with different dynamics parameters and motor noise variations, by introducing a framework…
Motion Generation Considering Situation with Conditional Generative Adversarial Networks for Throwing Robots
Kyo Kutsuzawa, Hitoshi Kusano, Ayaka Kume +1
When robots work in a cluttered environment, the constraints for motions change frequently and the required action can change even for the same task. However, planning complex moti…
Data Interpolating Prediction: Alternative Interpretation of Mixup
Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi +1
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks. However, this strategy is not always effective due to the gap between a…
Semi-flat minima and saddle points by embedding neural networks to overparameterization
Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake +1
We theoretically study the landscape of the training error for neural networks in overparameterized cases. We consider three basic methods for embedding a network into a wider one…
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita +1
Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure. In this paper, we present a novel hype…