Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Umeda, Yuhei"'
Autor:
Ramasubramanian, Shrinivas, Rangwani, Harsh, Takemori, Sho, Samanta, Kunal, Umeda, Yuhei, Radhakrishnan, Venkatesh Babu
The rise in internet usage has led to the generation of massive amounts of data, resulting in the adoption of various supervised and semi-supervised machine learning algorithms, which can effectively utilize the colossal amount of data to train model
Externí odkaz:
http://arxiv.org/abs/2403.18301
Autor:
Oulhaj, Ziyad, Ishii, Yoshiyuki, Ohga, Kento, Yamazaki, Kimihiro, Wada, Mutsuyo, Umeda, Yuhei, Kato, Takashi, Wada, Yuichiro, Kurihara, Hiroaki
Acquiring plausible pathways on high-dimensional structural distributions is beneficial in several domains. For example, in the drug discovery field, a protein conformational pathway, i.e. a highly probable sequence of protein structural changes, is
Externí odkaz:
http://arxiv.org/abs/2402.19177
Autor:
Iijima, Naoki, Imamura, Satoshi, Morita, Mikio, Takemori, Sho, Kasagi, Akihiko, Umeda, Yuhei, Yoshida, Eiji
Variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for noisy intermediate-scale quantum (NISQ) computers. It is promising for quantum chemical calculations (QCC) because it can calculate the ground-state energy of
Externí odkaz:
http://arxiv.org/abs/2311.09634
Autor:
Rangwani, Harsh, Ramasubramanian, Shrinivas, Takemori, Sho, Takashi, Kato, Umeda, Yuhei, Radhakrishnan, Venkatesh Babu
Self-training based semi-supervised learning algorithms have enabled the learning of highly accurate deep neural networks, using only a fraction of labeled data. However, the majority of work on self-training has focused on the objective of improving
Externí odkaz:
http://arxiv.org/abs/2304.14738
Autor:
Nakamura, Kota, Matsubara, Yasuko, Kawabata, Koki, Umeda, Yuhei, Wada, Yuichiro, Sakurai, Yasushi
Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and drop-off locations, time), how can we summarize large, dynamic high-or
Externí odkaz:
http://arxiv.org/abs/2303.03789
Self-learning Monte Carlo (SLMC) methods are recently proposed to accelerate Markov chain Monte Carlo (MCMC) methods using a machine learning model. With latent generative models, SLMC methods realize efficient Monte Carlo updates with less autocorre
Externí odkaz:
http://arxiv.org/abs/2211.14024
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs
Publikováno v:
2021 International Joint Conference on Artificial Intelligence, Aug 2021, Montr{\'e}al, Canada
Although neural networks are capable of reaching astonishing performances on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can be expensive from a computational perspective. In industrial applicati
Externí odkaz:
http://arxiv.org/abs/2105.04404
This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of abnormal heartbeats, known as arrhythmia. We strongly insist on gener
Externí odkaz:
http://arxiv.org/abs/1906.05795
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science. However, since the (metric) space of persistence dia
Externí odkaz:
http://arxiv.org/abs/1904.09378
Autor:
Anai, Hirokazu, Chazal, Frédéric, Glisse, Marc, Ike, Yuichi, Inakoshi, Hiroya, Tinarrage, Raphaël, Umeda, Yuhei
Publikováno v:
Topological Data Analysis: The Abel Symposium 2018
Despite strong stability properties, the persistent homology of filtrations classically used in Topological Data Analysis, such as, e.g. the Cech or Vietoris-Rips filtrations, are very sensitive to the presence of outliers in the data from which they
Externí odkaz:
http://arxiv.org/abs/1811.04757