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ORCIDMasanari Kimura
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2020 – today
- 2024
[j5]Masanari Kimura, Hideitsu Hino:
A Short Survey on Importance Weighting for Machine Learning. Trans. Mach. Learn. Res. 2024 (2024)- [i24]Masanari Kimura:
Understanding Test-Time Augmentation. CoRR abs/2402.06892 (2024) - [i23]Masanari Kimura, Hideitsu Hino:
A Short Survey on Importance Weighting for Machine Learning. CoRR abs/2403.10175 (2024) - [i22]Masanari Kimura, Ryotaro Shimizu, Yuki Hirakawa, Ryosuke Goto, Yuki Saito:
On permutation-invariant neural networks. CoRR abs/2403.17410 (2024) - [i21]Masanari Kimura, Hiroki Naganuma:
Geometric Insights into Focal Loss: Reducing Curvature for Enhanced Model Calibration. CoRR abs/2405.00442 (2024) - [i20]Yuya Yoshikawa, Masanari Kimura, Ryotaro Shimizu, Yuki Saito:
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property. CoRR abs/2405.14522 (2024) - [i19]Masanari Kimura, Howard D. Bondell:
Density Ratio Estimation via Sampling along Generalized Geodesics on Statistical Manifolds. CoRR abs/2406.18806 (2024) - [i18]Ryotaro Shimizu, Yu Wang, Masanari Kimura, Yuki Hirakawa, Takashi Wada, Yuki Saito, Julian J. McAuley:
A Fashion Item Recommendation Model in Hyperbolic Space. CoRR abs/2409.02599 (2024) - [i17]Masanari Kimura, Howard D. Bondell:
Test-Time Augmentation Meets Variational Bayes. CoRR abs/2409.12587 (2024) - 2023
- [j4]Masanari Kimura
:
On the Decomposition of Covariate Shift Assumption for the Set-to-Set Matching. IEEE Access 11: 120728-120740 (2023) - [c10]Masanari Kimura, Takuma Nakamura, Yuki Saito:
SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts. CVPR Workshops 2023: 3508-3513 - [i16]Masanari Kimura:
Generalization Bounds for Set-to-Set Matching with Negative Sampling. CoRR abs/2302.12991 (2023) - [i15]Masanari Kimura, Hideitsu Hino:
Information Geometrically Generalized Covariate Shift Adaptation. CoRR abs/2304.09387 (2023) - 2022
- [j3]Masanari Kimura, Hideitsu Hino:
Information Geometrically Generalized Covariate Shift Adaptation. Neural Comput. 34(9): 1944-1977 (2022) - [c9]Masanari Kimura:
Generalization Bounds for Set-to-Set Matching with Negative Sampling. ICONIP (4) 2022: 468-476 - [i14]Waku Hatakeyama, Shirou Kawakita, Ryohei Izawa, Masanari Kimura:
GRASP EARTH: Intuitive Software for Discovering Changes on the Planet. CoRR abs/2203.00955 (2022) - [i13]Masanari Kimura, Hideitsu Hino:
Information Geometry of Dropout Training. CoRR abs/2206.10936 (2022) - [i12]Ryotaro Shimizu, Masanari Kimura, Masayuki Goto:
Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding. CoRR abs/2210.17417 (2022) - 2021
- [j2]Masanari Kimura:
Generalized t-SNE Through the Lens of Information Geometry. IEEE Access 9: 129619-129625 (2021) - [j1]Masanari Kimura, Hideitsu Hino:
α-Geodesical Skew Divergence. Entropy 23(5): 528 (2021) - [c8]Masanari Kimura:
Why Mixup Improves the Model Performance. ICANN (2) 2021: 275-286 - [c7]Masanari Kimura:
Understanding Test-Time Augmentation. ICONIP (1) 2021: 558-569 - [c6]Masanari Kimura, Ryohei Izawa:
Density-Fixing: Simple yet Effective Regularization Method based on the Class Priors. IJCNN 2021: 1-8 - [i11]Masanari Kimura, Hideitsu Hino:
$α$-Geodesical Skew Divergence. CoRR abs/2103.17060 (2021) - [i10]Masanari Kimura, Takuma Nakamura, Yuki Saito:
SHIFT15M: Multiobjective Large-Scale Fashion Dataset with Distributional Shifts. CoRR abs/2108.12992 (2021) - 2020
- [c5]Masanari Kimura, Kei Wakabayashi, Atsuyuki Morishima:
Batch Prioritization of Data Labeling Tasks for Training Classifiers. HCOMP 2020: 163-167 - [c4]Masanari Kimura, Masayuki Tanaka:
New Perspective of Interpretability of Deep Neural Networks. ICICT 2020: 78-85 - [i9]Masanari Kimura:
Mixup Training as the Complexity Reduction. CoRR abs/2006.06231 (2020) - [i8]Masanari Kimura, Ryohei Izawa:
Density Fixing: Simple yet Effective Regularization Method based on the Class Prior. CoRR abs/2007.03899 (2020)
2010 – 2019
- 2019
- [c3]Masanari Kimura, Masayuki Tanaka:
Interpretation of Feature Space using Multi-Channel Attentional Sub-Networks. CVPR Workshops 2019: 36-39 - [i7]Masanari Kimura, Masayuki Tanaka:
Interpretation of Feature Space using Multi-Channel Attentional Sub-Networks. CoRR abs/1904.13078 (2019) - [i6]Masanari Kimura, Masayuki Tanaka:
Intentional Attention Mask Transformation for Robust CNN Classification. CoRR abs/1905.02719 (2019) - [i5]Masanari Kimura:
PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure. CoRR abs/1905.10939 (2019) - [i4]Masanari Kimura, Masayuki Tanaka:
New Perspective of Interpretability of Deep Neural Networks. CoRR abs/1909.07156 (2019) - [i3]Masanari Kimura:
Large-Scale Landslides Detection from Satellite Images with Incomplete Labels. CoRR abs/1910.07129 (2019) - 2018
- [c2]Masanari Kimura, Takashi Yanagihara:
Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data. ACCV Workshops 2018: 373-385 - [c1]Kento Nozawa, Masanari Kimura, Atsunori Kanemura:
Analyzing Centralities of Embedded Nodes. ICDM Workshops 2018: 1046-1049 - [i2]Kento Nozawa, Masanari Kimura, Atsunori Kanemura:
Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms. CoRR abs/1802.06368 (2018) - [i1]Masanari Kimura, Takashi Yanagihara:
Semi-supervised Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data. CoRR abs/1807.01136 (2018)
Coauthor Index
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