no code implementations • 9 Apr 2024 • Yuka Hashimoto, Ryuichiro Hataya
This interaction enables the circuits to share information among them, which contributes to improved generalization performance in machine learning tasks.
no code implementations • 5 Feb 2024 • Ryuichiro Hataya, Yoshinobu Kawahara
Through numerical experiments of hyperparameter optimization, including optimization of optimizers, we demonstrate the effectiveness of the glocal hypergradient estimation.
no code implementations • 3 Feb 2024 • Han Bao, Ryuichiro Hataya, Ryo Karakida
To this end, we characterize the notion of attention localization by the eigenspectrum of query-key parameter matrices and reveal that a small eigenspectrum variance leads attention to be localized.
1 code implementation • 17 Jul 2023 • Hiroki Naganuma, Ryuichiro Hataya, Ioannis Mitliagkas
In the realm of out-of-distribution (OOD) generalization tasks, fine-tuning pre-trained models has become a prevalent strategy.
1 code implementation • 29 Jun 2023 • Leonardo Placidi, Ryuichiro Hataya, Toshio Mori, Koki Aoyama, Hayata Morisaki, Kosuke Mitarai, Keisuke Fujii
In fact, also the Machine Learning research related to quantum computers undertakes a dual challenge: enhancing machine learning exploiting the power of quantum computers, while also leveraging state-of-the-art classical machine learning methodologies to help the advancement of quantum computing.
noisy quantum circuit classification (quantum ML, error mitigation) quantum circuit classification (classical ML) +1
1 code implementation • 7 Mar 2023 • Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya, Takaaki Mizuno, Mototaka Miyake, Hirokazu Watanabe, Masamichi Takahashi, Yasuyuki Takamizawa, Yukihiro Yoshida, Satoshi Nakamura, Nobuji Kouno, Amina Bolatkan, Yusuke Kurose, Tatsuya Harada, Ryuji Hamamoto
As a result, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples.
2 code implementations • 20 Feb 2023 • Ryuichiro Hataya, Makoto Yamada
The essential difficulty of gradient-based bilevel optimization using implicit differentiation is to estimate the inverse Hessian vector product with respect to neural network parameters.
no code implementations • 26 Jan 2023 • Ryuichiro Hataya, Yuka Hashimoto
We propose a new generalization of neural networks with noncommutative $C^*$-algebra.
1 code implementation • ICCV 2023 • Ryuichiro Hataya, Han Bao, Hiromi Arai
These trends lead us to a research question: "\textbf{will such generated images impact the quality of future datasets and the performance of computer vision models positively or negatively?}"
no code implementations • 29 Sep 2021 • Ryuichiro Hataya, Hideki Nakayama
Optimizing hyperparameters of machine learning algorithms especially for limited labeled data is important but difficult, because then obtaining enough validation data is practically impossible.
no code implementations • 23 Mar 2021 • Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Mototaka Miyake, Masamichi Takahashi, Akiko Nakagawa, Tatsuya Harada, Ryuji Hamamoto
To support comparative diagnostic reading, content-based image retrieval (CBIR), which can selectively utilize normal and abnormal features in medical images as two separable semantic components, will be useful.
no code implementations • ICLR Workshop EBM 2021 • Ryuichiro Hataya, Hideki Nakayama, Kazuki Yoshizoe
We present Graph Energy-based Model (GEM), an energy-based model for molecular graph generation.
no code implementations • 9 Feb 2021 • Ryuichiro Hataya, Hideki Nakayama, Kazuki Yoshizoe
It is common practice for chemists to search chemical databases based on substructures of compounds for finding molecules with desired properties.
no code implementations • 1 Jan 2021 • Ryuichiro Hataya, Hideki Nakayama
Convolutional Neural Networks (CNNs) are vulnerable to unseen noise on input images at the test time, and thus improving the robustness is crucial.
no code implementations • 12 Nov 2020 • Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Tatsuya Harada, Ryuji Hamamoto
Medical images can be decomposed into normal and abnormal features, which is considered as the compositionality.
no code implementations • 14 Jun 2020 • Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama
Data augmentation policies drastically improve the performance of image recognition tasks, especially when the policies are optimized for the target data and tasks.
1 code implementation • 26 May 2020 • Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Amina Bolatkan, Mototaka Miyake, Hirokazu Watanabe, Masamichi Takahashi, Jun Itami, Tatsuya Harada, Ryuji Hamamoto
In addition, we devise a metric to evaluate the anatomical fidelity of the reconstructed images and confirm that the overall detection performance is improved when the image reconstruction network achieves a higher score.
1 code implementation • ECCV 2020 • Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, Hideki Nakayama
In this paper, we propose a differentiable policy search pipeline for data augmentation, which is much faster than previous methods.
Ranked #1 on Data Augmentation on CIFAR-10
no code implementations • ICLR 2019 • Ryuichiro Hataya, Hideki Nakayama
Deep convolutional neural networks (CNNs) are known to be robust against label noise on extensive datasets.
no code implementations • 17 Apr 2019 • Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga
The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations.
no code implementations • 29 Mar 2019 • Leonardo Rundo, Changhee Han, Jin Zhang, Ryuichiro Hataya, Yudai Nagano, Carmelo Militello, Claudio Ferretti, Marco S. Nobile, Andrea Tangherloni, Maria Carla Gilardi, Salvatore Vitabile, Hideki Nakayama, Giancarlo Mauri
Prostate cancer is the most common cancer among US men.
no code implementations • ICLR Workshop LLD 2019 • Ryuichiro Hataya, Hideki Nakayama
In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt.