1 code implementation • Findings (EMNLP) 2021 • Kouta Nakayama, Shuhei Kurita, Akio Kobayashi, Yukino Baba, Satoshi Sekine
In this research, we propose a scheme to utilize all those systems which participated in the shared tasks.
no code implementations • 30 Apr 2024 • Yuto Nakashima, Mingzhe Yang, Yukino Baba
Generating preferred images using generative adversarial networks (GANs) is challenging owing to the high-dimensional nature of latent space.
no code implementations • 2 May 2023 • Rina Kagawa, Masaki Matsubara, Rei Miyata, Takuya Matsuzaki, Yukino Baba, Yoko Yamakata
This study proposed the experimental framework to identify useful contents of documents by aggregating lay readers' insights.
no code implementations • 8 Feb 2021 • Yota Ueda, Kazuki Fujii, Yuki Saito, Shinnosuke Takamichi, Yukino Baba, Hiroshi Saruwatari
A DNN-based generator is trained using a human-based discriminator, i. e., humans' perceptual evaluations, instead of the GAN's DNN-based discriminator.
no code implementations • 1 Aug 2020 • Yukino Baba, Jiyi Li, Hisashi Kashima
We propose an approach, called CrowDEA, which estimates the embeddings of the ideas in the multiple-criteria preference space, the best viewpoint for each idea, and preference criterion for each evaluator, to obtain a set of frontier ideas.
1 code implementation • 27 Jun 2020 • Mingzhe Yang, Yukino Baba
In this study, we apply these to iterative machine teaching for estimating the true labels of teaching examples along with student models that are used for teaching.
no code implementations • 25 Sep 2019 • Kazuki Fujii, Yuki Saito, Shinnosuke Takamichi, Yukino Baba, Hiroshi Saruwatari
To model the human-acceptable distribution, we formulate a backpropagation-based generator training algorithm by regarding human perception as a black-boxed discriminator.
no code implementations • 4 Oct 2018 • Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining.
1 code implementation • 4 Jul 2018 • Hirotaka Akita, Kosuke Nakago, Tomoki Komatsu, Yohei Sugawara, Shin-ichi Maeda, Yukino Baba, Hisashi Kashima
A possible approach to answer this question is to visualize evidence substructures responsible for the predictions.