Zobrazeno 1 - 10
of 39 303
pro vyhledávání: '"A. Okuno"'
Continuous normalizing flows (CNFs) can model data distributions with expressive infinite-length architectures. But this modeling involves computationally expensive process of solving an ordinary differential equation (ODE) during maximum likelihood
Externí odkaz:
http://arxiv.org/abs/2410.09246
Autor:
Zhang, Yuan, Fan, Chun-Kai, Ma, Junpeng, Zheng, Wenzhao, Huang, Tao, Cheng, Kuan, Gudovskiy, Denis, Okuno, Tomoyuki, Nakata, Yohei, Keutzer, Kurt, Zhang, Shanghang
In vision-language models (VLMs), visual tokens usually consume a significant amount of computational overhead, despite their sparser information density compared to text tokens. To address this, most existing methods learn a network to prune redunda
Externí odkaz:
http://arxiv.org/abs/2410.04417
Autor:
Vergnaud, C., Tiwari, V., Ren, L., Taniguchi, T., Watanabe, K., Okuno, H., de Moraes, I. Gomes, Marty, A., Robert, C., Marie, X., Jamet, M.
Transition metal dichalcogenides (TMD) like MoSe$_2$ exhibit remarkable optical properties such as intense photoluminescence (PL) in the monolayer form. To date, narrow-linewidth PL is only achieved in micrometer-sized exfoliated TMD flakes encapsula
Externí odkaz:
http://arxiv.org/abs/2407.12944
Autor:
Okuno, Akifumi
This paper presents an integrated perspective on robustness in regression. Specifically, we examine the relationship between traditional outlier-resistant robust estimation and robust optimization, which focuses on parameter estimation resistant to i
Externí odkaz:
http://arxiv.org/abs/2407.10418
Autor:
Yang, Huanrui, Huang, Yafeng, Dong, Zhen, Gudovskiy, Denis A, Okuno, Tomoyuki, Nakata, Yohei, Du, Yuan, Keutzer, Kurt, Zhang, Shanghang
The impact of quantization on the overall performance of deep learning models is a well-studied problem. However, understanding and mitigating its effects on a more fine-grained level is still lacking, especially for harder tasks such as object detec
Externí odkaz:
http://arxiv.org/abs/2407.03442
Normalizing flow-based generative models have been widely used in applications where the exact density estimation is of major importance. Recent research proposes numerous methods to improve their expressivity. However, conditioning on a context is l
Externí odkaz:
http://arxiv.org/abs/2406.00578
Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction
Autor:
Tosaki, Taisei, Uchino, Eiichiro, Kojima, Ryosuke, Mineharu, Yohei, Arita, Mikio, Miyai, Nobuyuki, Tamada, Yoshinori, Mikami, Tatsuya, Murashita, Koichi, Nakaji, Shigeyuki, Okuno, Yasushi
Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, the prediction effectiveness is hindered by dataset shift, which involves discrepancies in data distribution between the training
Externí odkaz:
http://arxiv.org/abs/2405.19864
Autor:
Ligaud, Clotilde, Van-Jodin, Lucie Le, Reig, Bruno, Trousset, Pierre, Brunet, Paul, Bertucchi, Michaël, Hellion, Clémence, Gauthier, Nicolas, Van-Hoan, Le, Okuno, Hanako, Dosenovic, Djordje, Cadot, Stéphane, Gassilloud, Remy, Jamet, Matthieu
Two-dimensional (2D) materials like transition metal dichalcogenides (TMD) have proved to be serious candidates to replace silicon in several technologies with enhanced performances. In this respect, the two remaining challenges are the wafer scale g
Externí odkaz:
http://arxiv.org/abs/2405.05693
Publikováno v:
Phys. Rev. A 110, 022404 (2024)
Among various quantum machine learning (QML) algorithms, the quantum kernel method has especially attracted attention due to its compatibility with noisy intermediate-scale quantum devices and its potential to achieve quantum advantage. This method p
Externí odkaz:
http://arxiv.org/abs/2405.01086
Autor:
Tomoda, Hiroko, Machinaga, Akihiro, Takase, Kan, Harada, Jun, Kashiwazaki, Takahiro, Umeki, Takeshi, Miki, Shigehito, China, Fumihiro, Yabuno, Masahiro, Terai, Hirotaka, Okuno, Daichi, Takeda, Shuntaro
In optical quantum information processing with continuous variables, optical non-Gaussian quantum states are essential for universal and fault-tolerant quantum computation. Experimentally, their most typical generation method is photon subtraction (P
Externí odkaz:
http://arxiv.org/abs/2404.19304