3 citations · 19 across the 12 of their papers we have counts for
12 papers
A Fashion Item Recommendation Model in Hyperbolic Space
Ryotaro Shimizu, Yu Wang, Masanari Kimura +4
In this work, we propose a fashion item recommendation model that incorporates hyperbolic geometry into user and item representations. Using hyperbolic space, our model aims to cap…
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…
Generalization Bounds for Set-to-Set Matching with Negative Sampling
Masanari Kimura
The problem of matching two sets of multiple elements, namely set-to-set matching, has received a great deal of attention in recent years. In particular, it has been reported that…
Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding
Ryotaro Shimizu, Masanari Kimura, Masayuki Goto
Several techniques to map various types of components, such as words, attributes, and images, into the embedded space have been studied. Most of them estimate the embedded represen…
Information Geometry of Dropout Training
Masanari Kimura, Hideitsu Hino
Dropout is one of the most popular regularization techniques in neural network training. Because of its power and simplicity of idea, dropout has been analyzed extensively and many…
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…
-Geodesical Skew Divergence
Masanari Kimura, Hideitsu Hino
The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree determined by the parameter , with the other distribution. Such divergence is an approx…
Why Mixup Improves the Model Performance
Masanari Kimura
Machine learning techniques are used in a wide range of domains. However, machine learning models often suffer from the problem of over-fitting. Many data augmentation methods have…