29 citations · 58 across the 8 of their papers we have counts for
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cs.LG2021★ 3 cited
Semi-supervised learning by selective training with pseudo labels via confidence estimation
Masato Ishii
We propose a novel semi-supervised learning (SSL) method that adopts selective training with pseudo labels. In our method, we generate hard pseudo-labels and also estimate their co…
cs.LG2021★ 5 cited
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
Takuya Narihira, Javier Alonsogarcia, Fabien Cardinaux +14
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design…
cs.LG2019★ 4 cited
Zero-shot Domain Adaptation Based on Attribute Information
Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
In this paper, we propose a novel domain adaptation method that can be applied without target data. We consider the situation where domain shift is caused by a prior change of a sp…