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Iwata, Tomoharu
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1

Meta-learning to calibrate Gaussian processes with deep ker..:

Iwata, Tomoharu ; Kumagai, Atsutoshi
Neurocomputing.  579 (2024)  - p. 127441 , 2024
Link: https://doi.org/10.1016/..
 
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2

Meta-learning for heterogeneous treatment effect estimation..:

Iwata, Tomoharu ; Chikahara, Yoichi
Machine Learning.  , 2024
Link: https://doi.org/10.1007/..
 
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3

Meta-learning representations for clustering with infinite ..:

Iwata, Tomoharu
Neurocomputing.  549 (2023)  - p. 126423 , 2023
Link: https://doi.org/10.1016/..
 
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4

Gaussian Process Regression With Interpretable Sample-Wise ..:

Yoshikawa, Yuya ; Iwata, Tomoharu
IEEE Transactions on Neural Networks and Learning Systems.  34 (2023)  9 - p. 5789-5803 , 2023
Link: https://doi.org/10.1109/..
 
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5

Transfer Anomaly Detection for Maximizing the Partial AUC:

, In: 2022 International Joint Conference on Neural Networks (IJCNN),
Kumagai, Atsutoshi ; Iwata, Tomoharu ; Nishiyama, Taishi. - p. 1-8 , 2022
Link: https://doi.org/10.1109/..
 
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6

Tight Integration Of Neural- And Clustering-Based Diarizati..:

, In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Kinoshita, Keisuke ; Delcroix, Marc ; Iwata, Tomoharu - p. 8382-8386 , 2022
Link: https://doi.org/10.1109/..
 
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7

Predicting Opinion Dynamics via Sociologically-Informed Neu..:

, In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,
Okawa, Maya ; Iwata, Tomoharu - p. 1306-1316 , 2022
Link: https://dl.acm.org/doi/1..
 
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8

Learning Optimal Priors for Task-Invariant Representations ..:

, In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining,
Takahashi, Hiroshi ; Iwata, Tomoharu ; Kumagai, Atsutoshi... - p. 1739-1748 , 2022
Link: https://dl.acm.org/doi/1..
 
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9

Multi-Source Domain Generalization Using Domain Attributes ..:

TAWARA, Naohiro ; OGAWA, Atsunori ; IWATA, Tomoharu...
IEICE Transactions on Information and Systems.  E105.D (2022)  1 - p. 150-160 , 2022
Link: https://doi.org/10.1587/..
 
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10

Context-aware spatio-temporal event prediction via convolut..:

Okawa, Maya ; Iwata, Tomoharu ; Tanaka, Yusuke...
Machine Learning.  111 (2022)  8 - p. 2929-2950 , 2022
Link: https://doi.org/10.1007/..
 
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11

Dynamic mode decomposition via convolutional autoencoders f..:

Ul Haq, Israr ; Iwata, Tomoharu ; Kawahara, Yoshinobu
Computer Vision and Image Understanding.  216 (2022)  - p. 103355 , 2022
Link: https://doi.org/10.1016/..
 
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12

Different cell imaging methods did not significantly improv..:

Ogawa, Taisaku ; Ochiai, Koji ; Iwata, Tomoharu...
PLOS ONE.  17 (2022)  1 - p. e0262397 , 2022
Link: https://doi.org/10.1371/..
 
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13

Neural generators of sparse local linear models for achievi..:

Yoshikawa, Yuya ; Iwata, Tomoharu
Information Fusion.  81 (2022)  - p. 116-128 , 2022
Link: https://doi.org/10.1016/..
 
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14

Deep Reinforcement Learning for Pedestrian Guidance:

, In: PRIMA 2020: Principles and Practice of Multi-Agent Systems; Lecture Notes in Computer Science,
Shimizu, Hitoshi ; Hara, Takanori ; Iwata, Tomoharu - p. 334-342 , 2021
Link: https://doi.org/10.1007/..
 
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15

Few-shot learning for spatial regression via neural embeddi..:

Iwata, Tomoharu ; Tanaka, Yusuke
Machine Learning.  111 (2021)  4 - p. 1239-1257 , 2021
Link: https://doi.org/10.1007/..
 
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