no code implementations • 6 Apr 2020 • Kenya Sakka, Kotaro Nakayama, Nisei Kimura, Taiki Inoue, Yusuke Iwasawa, Ryohei Yamaguchi, Yosimasa Kawazoe, Kazuhiko Ohe, Yutaka Matsuo
And, we were confirmed from the generated findings that the proposed method was able to consider the orthographic variants.
no code implementations • 26 Jan 2018 • Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
However, we found that when this model attempts to generate a large dimensional modality missing at the input, the joint representation collapses and this modality cannot be generated successfully.
no code implementations • ICLR 2018 • Joji Toyama, Yusuke Iwasawa, Kotaro Nakayama, Yutaka Matsuo
The partial reward function is a reward function for a partial sequence of a certain length.
no code implementations • ICLR 2018 • Yusuke Iwasawa, Kotaro Nakayama, Yutaka Matsuo
AFL learn such a representations by training the networks to deceive the adversary that predict the sensitive information from the network, and therefore, the success of the AFL heavily relies on the choice of the adversary.
no code implementations • 9 Jun 2017 • Mohammadamin Barekatain, Miquel Martí, Hsueh-Fu Shih, Samuel Murray, Kotaro Nakayama, Yutaka Matsuo, Helmut Prendinger
Despite significant progress in the development of human action detection datasets and algorithms, no current dataset is representative of real-world aerial view scenarios.
no code implementations • 25 Nov 2016 • Joji Toyama, Masanori Misono, Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
The report of earlier studies has introduced a latent variable to capture the entire meaning of sentence and achieved improvement on attention-based Neural Machine Translation.
2 code implementations • 7 Nov 2016 • Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
As described herein, we propose a joint multimodal variational autoencoder (JMVAE), in which all modalities are independently conditioned on joint representation.
no code implementations • 10 Oct 2016 • Masatoshi Uehara, Issei Sato, Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
Generative adversarial networks (GANs) are successful deep generative models.