11 citations · 17 across the 8 of their papers we have counts for
8 papers
A Generalized Model for Multidimensional Intransitivity
Jiuding Duan, Jiyi Li, Yukino Baba +1
Intransitivity is a critical issue in pairwise preference modeling. It refers to the intransitive pairwise preferences between a group of players or objects that potentially form a…
An experimental framework for designing document structure for users' decision making -- An empirical study of recipes
Rina Kagawa, Masaki Matsubara, Rei Miyata +3
Textual documents need to be of good quality to ensure effective asynchronous communication in remote areas, especially during the COVID-19 pandemic. However, defining a preferred…
HumanACGAN: conditional generative adversarial network with human-based auxiliary classifier and its evaluation in phoneme perception
Yota Ueda, Kazuki Fujii, Yuki Saito +3
We propose a conditional generative adversarial network (GAN) incorporating humans' perceptual evaluations. A deep neural network (DNN)-based generator of a GAN can represent a rea…
CrowDEA: Multi-view Idea Prioritization with Crowds
Yukino Baba, Jiyi Li, Hisashi Kashima
Given a set of ideas collected from crowds with regard to an open-ended question, how can we organize and prioritize them in order to determine the preferred ones based on preferen…
Iterative Machine Teaching without Teachers
Mingzhe Yang, Yukino Baba
Iterative machine teaching is a method for selecting an optimal teaching example that enables a student to efficiently learn a target concept at each iteration. Existing studies on…
HumanGAN: generative adversarial network with human-based discriminator and its evaluation in speech perception modeling
Kazuki Fujii, Yuki Saito, Shinnosuke Takamichi +2
We propose the HumanGAN, a generative adversarial network (GAN) incorporating human perception as a discriminator. A basic GAN trains a generator to represent a real-data distribut…
Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki +4
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature ex…
BayesGrad: Explaining Predictions of Graph Convolutional Networks
Hirotaka Akita, Kosuke Nakago, Tomoki Komatsu +4
Recent advances in graph convolutional networks have significantly improved the performance of chemical predictions, raising a new research question: "how do we explain the predict…