most citedA Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning

49 citations · 63 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20193 cited

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…

cs.RO2019

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…

cs.LG20198 cited

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…

cs.LG20193 cited

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…

stat.ML201949 cited

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…