1 citations · 2 across the 20 of their papers we have counts for
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Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learning
Kohki Akiba, Shinnosuke Matsuo, Shota Harada +1
Semi-supervised learning (SSL) often suffers under class imbalance, where pseudo-labeling amplifies majority bias and suppresses minority performance. We address this issue with a…
Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection
Kaito Shiku, Kazuya Nishimura, Shinnosuke Matsuo +2
Spatial transcriptomics (ST) is a novel technology that enables the observation of gene expression at the resolution of individual spots within pathological tissues. ST quantifies…
Weakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-Labeling
Takumi Okuo, Shinnosuke Matsuo, Shota Harada +2
Domain shift is a significant challenge in machine learning, particularly in medical applications where data distributions differ across institutions due to variations in data coll…
Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags
Shunsuke Kubo, Shinnosuke Matsuo, Daiki Suehiro +4
Learning from label proportions (LLP) is a kind of weakly supervised learning that trains an instance-level classifier from label proportions of bags, which consist of sets of inst…
Test-Time Augmentation for Traveling Salesperson Problem
Ryo Ishiyama, Takahiro Shirakawa, Seiichi Uchida +1
We propose Test-Time Augmentation (TTA) as an effective technique for addressing combinatorial optimization problems, including the Traveling Salesperson Problem. In general, deep…
Dynamic Data Augmentation with Gating Networks for Time Series Recognition
Daisuke Oba, Shinnosuke Matsuo, Brian Kenji Iwana
Data augmentation is a technique to improve the generalization ability of machine learning methods by increasing the size of the dataset. However, since every augmentation method i…