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ORCIDTaiki Miyagawa
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2020 – today
- 2025
[c12]Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka:
Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test. ICLR 2025- [c11]Junki Mori
, Kosuke Kihara, Taiki Miyagawa, Akinori F. Ebihara, Isamu Teranishi, Hisashi Kashima:
Federated Source-Free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data. WACV 2025: 6879-6889 - [i12]Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka:
Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test. CoRR abs/2501.18059 (2025) - [i11]Takuma Amada, Kazuya Kakizaki, Taiki Miyagawa, Akinori F. Ebihara, Kaede Shiohara, Toshihiko Yamasaki:
Robust Deepfake Detection for Electronic Know Your Customer Systems Using Registered Images. CoRR abs/2507.22601 (2025) - [i10]Kosuke Kihara, Junki Mori, Taiki Miyagawa, Akinori F. Ebihara:
Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Models. CoRR abs/2509.08372 (2025) - 2024
- [c10]Hideyuki Oiso, Yuto Matsunaga, Kazuya Kakizaki, Taiki Miyagawa:
Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset. INTERSPEECH 2024 - [c9]Taiki Miyagawa, Takeru Yokota:
Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees. NeurIPS 2024 - [c8]Yuto Matsunaga, Satoru Momiyama, Batnyam Enkhtaivan, Taiki Miyagawa, Isamu Teranishi:
ConvFL: Efficient Federated Learning of Heterogeneous Model Structures with Converters. SmartIoT 2024: 173-180 - [i9]Hideyuki Oiso, Yuto Matsunaga, Kazuya Kakizaki, Taiki Miyagawa:
Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset. CoRR abs/2410.09869 (2024) - [i8]Taiki Miyagawa, Takeru Yokota:
Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees. CoRR abs/2410.18153 (2024) - [i7]Junki Mori, Kosuke Kihara, Taiki Miyagawa, Akinori F. Ebihara, Isamu Teranishi, Hisashi Kashima:
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data. CoRR abs/2412.13757 (2024) - 2023
- [c7]Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka:
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification. ICASSP 2023: 1-5 - [c6]Hiroshi Fukui, Taiki Miyagawa, Yusuke Morishita:
Multi-Object Tracking as Attention Mechanism. ICIP 2023: 505-509 - [i6]Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka:
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification. CoRR abs/2302.09810 (2023) - [i5]Hiroshi Fukui, Taiki Miyagawa, Yusuke Morishita:
Multi-Object Tracking as Attention Mechanism. CoRR abs/2307.05874 (2023) - 2022
- [c5]Azusa Sawada, Taiki Miyagawa, Akinori F. Ebihara, Shoji Yachida, Toshinori Hosoi:
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series. IJCNN 2022: 1-8 - [c4]Taiki Miyagawa:
Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis. NeurIPS 2022 - [i4]Azusa Sawada, Taiki Miyagawa, Akinori F. Ebihara, Shoji Yachida, Toshinori Hosoi:
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series. CoRR abs/2207.03718 (2022) - [i3]Taiki Miyagawa:
Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis. CoRR abs/2210.15898 (2022) - 2021
- [c3]Kazuya Kakizaki, Taiki Miyagawa, Inderjeet Singh, Jun Sakuma:
Toward Practical Adversarial Attacks on Face Verification Systems. BIOSIG 2021: 113-124 - [c2]Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka:
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy. ICLR 2021 - [c1]Taiki Miyagawa, Akinori F. Ebihara:
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization. ICML 2021: 7792-7804 - [i2]Akinori F. Ebihara, Taiki Miyagawa:
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization. CoRR abs/2105.13636 (2021) - 2020
- [i1]Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai, Hitoshi Imaoka:
Deep Neural Networks for the Sequential Probability Ratio Test on Non-i.i.d. Data Series. CoRR abs/2006.05587 (2020)
Coauthor Index
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