3 citations · 19 across the 12 of their papers we have counts for
6 papers · 1 filter
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property
Yuya Yoshikawa, Masanari Kimura, Ryotaro Shimizu +1
Techniques that explain the predictions of black-box machine learning models are crucial to make the models transparent, thereby increasing trust in AI systems. The input features…
On permutation-invariant neural networks
Masanari Kimura, Ryotaro Shimizu, Yuki Hirakawa +2
Conventional machine learning algorithms have traditionally been designed under the assumption that input data follows a vector-based format, with an emphasis on vector-centric par…
A Short Survey on Importance Weighting for Machine Learning
Masanari Kimura, Hideitsu Hino
Importance weighting is a fundamental procedure in statistics and machine learning that weights the objective function or probability distribution based on the importance of the in…
Understanding Test-Time Augmentation
Masanari Kimura
Test-Time Augmentation (TTA) is a very powerful heuristic that takes advantage of data augmentation during testing to produce averaged output. Despite the experimental effectivenes…
Information Geometrically Generalized Covariate Shift Adaptation
Masanari Kimura, Hideitsu Hino
Many machine learning methods assume that the training and test data follow the same distribution. However, in the real world, this assumption is very often violated. In particular…
SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
Masanari Kimura, Takuma Nakamura, Yuki Saito
This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional item…