Results 41 to 50 of about 471,788 (278)
Optimizing Automated Trading Systems with Deep Reinforcement Learning
Algorithms, 2023 In this paper, we propose a novel approach to optimize parameters for strategies in automated trading systems. Based on the framework of Reinforcement learning, our work includes the development of a learning environment, state representation, reward ...
Minh Tran, Duc Pham-Hi, Marc Bui
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A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges
IEEE Access, 2022 In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing.
Abdullah A. Abdullah +2 more
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International Journal of Prognostics and Health Management, 2019 As more features are added to the heavy-duty construction equipment, its complexity increases and early fault detection of certain components becomes more challenging due to too many fault codes generated when a failure occurs.
Källström Elisabeth +4 more
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Bayesian multitask inverse reinforcement learning [PDF]
, 2011 We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task.
C.A. Rothkopf +3 more
core +4 more sources
Molecular Oncology, EarlyView.Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
Deep-learning jets with uncertainties and more
SciPost Physics, 2020 Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main features of Bayesian versions of established deep-learning taggers.
Sven Bollweg, Manuel Haussmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, Jennifer Thompson
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Revisiting Bayesian Autoencoders With MCMC
IEEE Access, 2022 Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust ...
Rohitash Chandra +3 more
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Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Statistical ScienceTransfer learning is a burgeoning concept in statistical machine learning that seeks to improve inference and/or predictive accuracy on a domain of interest by leveraging data from related domains. While the term "transfer learning" has garnered much recent interest, its foundational principles have existed for years under various guises.
Piotr M. Suder +2 more
openaire +3 more sources
Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer
Aging and Cancer, EarlyView.Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss +12 more
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