Zobrazeno 1 - 10
of 23 477
pro vyhledávání: '"Shirakawa, A."'
The majority of theoretical analyses of evolutionary algorithms in the discrete domain focus on binary optimization algorithms, even though black-box optimization on the categorical domain has a lot of practical applications. In this paper, we consid
Externí odkaz:
http://arxiv.org/abs/2407.07388
The covariance matrix adaptation evolution strategy (CMA-ES) is a stochastic search algorithm using a multivariate normal distribution for continuous black-box optimization. In addition to strong empirical results, part of the CMA-ES can be described
Externí odkaz:
http://arxiv.org/abs/2406.16506
The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful optimization method for continuous black-box optimization problems. Several noise-handling methods have been proposed to bring out the optimization performance of the CMA-ES o
Externí odkaz:
http://arxiv.org/abs/2405.11471
In several real-world applications in medical and control engineering, there are unsafe solutions whose evaluations involve inherent risk. This optimization setting is known as safe optimization and formulated as a specialized type of constrained opt
Externí odkaz:
http://arxiv.org/abs/2405.10534
Autor:
Shirakawa, Ken, Nagano, Yoshihiro, Tanaka, Misato, Aoki, Shuntaro C., Majima, Kei, Muraki, Yusuke, Kamitani, Yukiyasu
Advances in brain decoding, particularly visual image reconstruction, have sparked discussions about the societal implications and ethical considerations of neurotechnology. As these methods aim to recover visual experiences from brain activity and a
Externí odkaz:
http://arxiv.org/abs/2405.10078
Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variables, categorical variables do not necessarily have a meaningful order,
Externí odkaz:
http://arxiv.org/abs/2405.09962
Autor:
Robledo-Moreno, Javier, Motta, Mario, Haas, Holger, Javadi-Abhari, Ali, Jurcevic, Petar, Kirby, William, Martiel, Simon, Sharma, Kunal, Sharma, Sandeep, Shirakawa, Tomonori, Sitdikov, Iskandar, Sun, Rong-Yang, Sung, Kevin J., Takita, Maika, Tran, Minh C., Yunoki, Seiji, Mezzacapo, Antonio
A universal quantum computer can be used as a simulator capable of predicting properties of diverse quantum systems. Electronic structure problems in chemistry offer practical use cases around the hundred-qubit mark. This appears promising since curr
Externí odkaz:
http://arxiv.org/abs/2405.05068
We propose Test-Time Augmentation (TTA) as an effective technique for addressing combinatorial optimization problems, including the Traveling Salesperson Problem. In general, deep learning models possessing the property of invariance, where the outpu
Externí odkaz:
http://arxiv.org/abs/2405.04767
Autor:
Shirakawa, Toru, Li, Yi, Wu, Yulun, Qiu, Sky, Li, Yuxuan, Zhao, Mingduo, Iso, Hiroyasu, van der Laan, Mark
We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings. Our approach utilizes a transformer a
Externí odkaz:
http://arxiv.org/abs/2404.04399
In periodically driven (Floquet) systems, evolution typically results in an infinite-temperature thermal state due to continuous energy absorption over time. However, before reaching thermal equilibrium, such systems may transiently pass through a me
Externí odkaz:
http://arxiv.org/abs/2403.16718