89 citations · 192 across the 8 of their papers we have counts for
8 papers
Translation Between Waves, wave2wave
Tsuyoshi Okita, Hirotaka Hachiya, Sozo Inoue +1
The understanding of sensor data has been greatly improved by advanced deep learning methods with big data. However, available sensor data in the real world are still limited, whic…
Exchangeable deep neural networks for set-to-set matching and learning
Yuki Saito, Takuma Nakamura, Hirotaka Hachiya +1
Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract feat…
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration
Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt +2
The policy gradient approach is a flexible and powerful reinforcement learning method particularly for problems with continuous actions such as robot control. A common challenge in…
Feature Selection via L1-Penalized Squared-Loss Mutual Information
Wittawat Jitkrittum, Hirotaka Hachiya, Masashi Sugiyama
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to s…
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
Ning Xie, Hirotaka Hachiya, Masashi Sugiyama
Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation…
Parametric Return Density Estimation for Reinforcement Learning
Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima +2
Most conventional Reinforcement Learning (RL) algorithms aim to optimize decision-making rules in terms of the expected returns. However, especially for risk management purposes, o…