Zobrazeno 1 - 10
of 7 029
pro vyhledávání: '"Shafei, A."'
Domain generalization aims to learn a model from multiple training domains and generalize it to unseen test domains. Recent theory has shown that seeking the deep models, whose parameters lie in the flat minima of the loss landscape, can significantl
Externí odkaz:
http://arxiv.org/abs/2412.13573
Autor:
Dürholt, Johannes P., Asche, Thomas S., Kleinekorte, Johanna, Mancino-Ball, Gabriel, Schiller, Benjamin, Sung, Simon, Keupp, Julian, Osburg, Aaron, Boyne, Toby, Misener, Ruth, Eldred, Rosona, Costa, Wagner Steuer, Kappatou, Chrysoula, Lee, Robert M., Linzner, Dominik, Walz, David, Wulkow, Niklas, Shafei, Behrang
Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Previous BO implementations, for example as they exist in the literature
Externí odkaz:
http://arxiv.org/abs/2408.05040
Autor:
Gan, Jiayan, Du, Zhixing, Li, Qiang, Shao, Huaizong, Lin, Jingran, Pan, Ye, Wen, Zhongyi, Wang, Shafei
While the Internet of Things (IoT) technology is booming and offers huge opportunities for information exchange, it also faces unprecedented security challenges. As an important complement to the physical layer security technologies for IoT, radio fr
Externí odkaz:
http://arxiv.org/abs/2406.14869
Autor:
Qing, Jixiang, Langdon, Becky D, Lee, Robert M, Shafei, Behrang, van der Wilk, Mark, Tsay, Calvin, Misener, Ruth
We consider the problem of optimizing initial conditions and termination time in dynamical systems governed by unknown ordinary differential equations (ODEs), where evaluating different initial conditions is costly and the state's value can not be me
Externí odkaz:
http://arxiv.org/abs/2406.02352
De-interleaving of the mixtures of Hidden Markov Processes (HMPs) generally depends on its representation model. Existing representation models consider Markov chain mixtures rather than hidden Markov, resulting in the lack of robustness to non-ideal
Externí odkaz:
http://arxiv.org/abs/2406.00416
Robust optimisation is a well-established framework for optimising functions in the presence of uncertainty. The inherent goal of this problem is to identify a collection of inputs whose outputs are both desirable for the decision maker, whilst also
Externí odkaz:
http://arxiv.org/abs/2405.10221
The goal of multi-objective optimisation is to identify the Pareto front surface which is the set obtained by connecting the best trade-off points. Typically this surface is computed by evaluating the objectives at different points and then interpola
Externí odkaz:
http://arxiv.org/abs/2405.01404
Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems. Recently,
Externí odkaz:
http://arxiv.org/abs/2404.08566
Autor:
Folch, Jose Pablo, Tsay, Calvin, Lee, Robert M, Shafei, Behrang, Ormaniec, Weronika, Krause, Andreas, van der Wilk, Mark, Misener, Ruth, Mutný, Mojmír
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many real-life problems do not offer this flexibility; in particular, the sear
Externí odkaz:
http://arxiv.org/abs/2402.08406
Automatic Modulation Recognition (AMR) is a crucial technology in the domains of radar and communications. Traditional AMR approaches assume a closed-set scenario, where unknown samples are forcibly misclassified into known classes, leading to seriou
Externí odkaz:
http://arxiv.org/abs/2312.13023