Hello World!

I am Ryuichiro Hataya, PhD student at UTokyo, RA at RIKEN AIP, RIISE at UTokyo, and Beyond AI at UTokyo.

Research Interests

• Weakly-supervised Learning
• Meta Learning
• Energy-based Models
• Application of DL (Medical imaging, Palaeontology, Chemistry)

News

• Call for NeurIPS meetups is now out!
• I will present about Faster AutoAugment and its applications at AIP Open seminar.
• Our paper “Graph Energy-based Model for Molecular Graph Generation” is accepted at EBM workshop 2021 as a contributed talk.
• I will serve as a meetup chair for NeurIPS 2021.
• My research proposal has been accepted in JSPS’s travel grant.
• My research proposals have been accepted by Microsoft Research Asia, and RIISE at UTokyo.
• We organized a NeurIPS meetup and Women in ML in Japan: https://neuripsmeetupjapan.github.io.
• Our paper “Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval” is accepted at ML4H 2020.

Conference Papers (Peer-reviewed)

• Taiga Kashima, Ryuichiro Hataya, and Hideki Nakayama, “Visualizing Association in Exemplar-based Classification.” International Conference on Acoustics, Speech, and Signal Processing, 2021.
• Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama, “Faster AutoAugment: Learning Augmentation Strategies using Backpropagation." European Conference on Computer Vision, 2020.
• Ryuichiro Hataya, and Hideki Nakayama, “LOL: Learning To Optimize Loss Switching Under Label Noise.” International Conference on Image Processing, 2019.

Preprints

• Ryuichiro Hataya, Hideki Nakayama, and Kazuki Yoshizoe, “Graph Energy-based Model for Substructure Preserving Molecular Design.” 2021. arxiv
• Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama, “Meta Approach to Data Augmentation Optimization.” 2020. arXiv

Others

• Ryuichiro Hataya, Hideki Nakayama, and Kazuki Yoshizoe, “Graph Energy-based Model for Molecular Graph Generation." EBM Workshop at ICLR 2021, 2021. (Peer Reviwed, Contributed Talk)
• 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 Nideki Nakayama, “Unifying semi-supervised and robust leaning by mixup.” Workshop on Learning from Limited Labeled Data at ICLR 2019, 2019. (Peer Reviewed, Spotlight)

Invited Talks

• “Data Augmentation for Deep Learning” (in Japanese), Symposium on Sensing via Image Information, 2021.
• “Data Augmentation for Deep Learning” (in Japanese), StatsML Symposium, 2020.

Research Grants

• Research Project of Differentiable Data Augmentation for Image Recognition, Overseas Challenge Program for Young Researchers by JSPS, ¥1.4M, 2021.
• Research Project of Interactive Image Generation, Microsoft Research Asia Collaborative Research Program (D-CORE 2021) by MSRA, ¥1.0M, 2021.
• Research Project of Inclusive Image Recognition, Sprouting Research RA’s in Value Exchange Engineering by RIISE@UTokyo, ¥2.0M, 2020~2022.

Awards

• Microsoft Research Asia D-Core Award, 2020.
• Best Student Paper Award, The 23rd Meeting on Image Recognition and Understanding, 2020.

Others

• Meetup Chair of NeurIPS, 2021.
• Organizer of NeurIPS meetup Japan & Women in ML, 2020.
• Volunteer for ICML, and ICLR, 2020.
• Reviewer for NeurIPS, ICCV, ICLR, CVPR 2019~.