3 citations · 19 across the 12 of their papers we have counts for
4 papers · 2 filters
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…