no code implementations • 6 Jun 2021 • Shohei Ohsawa
We present a general framework for evolutionary learning to emergent unbiased state representation without any supervision.
no code implementations • 14 Jun 2019 • Makiko Mita, Kensuke Ito, Shohei Ohsawa, Hideyuki Tanaka
Our study provides a survey on how existing stablecoins-- cryptocurrencies aiming at price stabilization-- peg their value to other assets, from the perspective of Decentralized Payment Systems (DPSs).
Cryptography and Security
no code implementations • ICLR 2019 • Hirono Okamoto, Shohei Ohsawa, Itto Higuchi, Haruka Murakami, Mizuki Sango, Zhenghang Cui, Masahiro Suzuki, Hiroshi Kajino, Yutaka Matsuo
It reformulates the posterior with a natural paring $\langle, \rangle: \mathcal{Z} \times \mathcal{Z}^* \rightarrow \Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation.
no code implementations • ICLR Workshop DeepGenStruct 2019 • Hirono Okamoto, Masahiro Suzuki, Itto Higuchi, Shohei Ohsawa, Yutaka Matsuo
However, when the dimension of multiclass labels is large, these models cannot change images corresponding to labels, because learning multiple distributions of the corresponding class is necessary to transfer an image.
no code implementations • ICLR 2018 • Shohei Ohsawa, Kei Akuzawa, Tatsuya Matsushima, Gustavo Bezerra, Yusuke Iwasawa, Hiroshi Kajino, Seiya Takenaka, Yutaka Matsuo
Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments.