Our paper “An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration” (Naganuma & Hataya) has been accepted at ICCV Workshop 2023 on Uncertainty Quantification for Computer Vision.
I will visit Nicolaus Copernicus University Poland from Sep 25th to 30th.
Our paper “Will Large-scale Generative Models Corrupt Future Datasets?” has been accepted at ICCV 2023.
Our paper “Towards AI-driven radiology education: A self-supervised segmentation-based framework for high-precision medical image editing” (Kobayashi et al.) has been accepted at MICCAI 2023 as an oral presentation.
I will visit MILA at Montreal and attend CVPR in June.
I will give a talk at UTokyo ICEPP.
I will visit IIT at Genova in May.
I will attend AISTATS at Valencia in April.
I will visit Vietnam Institute for Advanced Study in Mathematics at Hanoi in April.
I will visit EPFL CIS (Switzerland) and Fraunhofer IIS (Germany) from 8th to 15th March 2023.
Our paper “Nyström Method for Accurate and Scalable Implicit Differentiation” has been accepted at AISTATS 2023.
Ryuichiro Hataya, and Hideki Nakayama, “DJMix: Unsupervised Task-agnostic Image Augmentation for Improving Robustness of Convolutional Neural Networks,” International Joint Conference on Neural Networks, 2022.
Leonardo Placidi, Ryuichiro Hataya, Toshio Mori, Koki Aoyama, Hayata Morisaki, Kosuke Mitarai, and Keisuke Fujii, “MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era,” 2023. arXiv
Ryuichiro Hataya, and Yuka Hashimoto, “Noncommutative $C^\star$-algebra Net: Learning Neural Networks with Powerful Product Structure in $C^\star$-algebra,” 2023. arXiv
Ryuichiro Hataya, Hideki Nakayama, and Kazuki Yoshizoe, “Graph Energy-based Model for Substructure Preserving Molecular Design,” 2021. arxiv
Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Tatsuya Harada, and Ryuji Hamamoto, “Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval.” Machine Learning for Health Workshop at NeurIPS 2020. (Peer Reviewed, Extended Abstract)
Ryuichiro Hataya, Kumiko Matsui, and Tomoki Karasawa, “Learning to Identify Large Fossils using Deep Convolutional Neural Networks”, Geological Society of America Abstracts with Programs. Vol 52, No. 6, 2020.
Ryuichiro Hataya, and Hideki Nakayama, “Unifying semi-supervised and robust leaning by mixup.” Workshop on Learning from Limited Labeled Data at ICLR 2019, 2019. (Peer Reviewed, Spotlight)