Results 41 to 50 of about 6,531,013 (293)
Mayo Clinic Proceedings: Digital HealthTo assess the current real-world applications of machine learning (ML) and artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that influence patient or consumer health behaviors.
Amy Bucher, PhD +2 more
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Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
IEEE Access, 2023 While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to ...
Maziar Gomrokchi +4 more
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Sample Efficient Reinforcement Learning with REINFORCE
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visitation measure based mini-batch stochastic gradients ...
Junzi Zhang +3 more
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Adaptive Control with Approximated Policy Search Approach
ITB Journal of Engineering Science, 2010 Most of existing adaptive control schemes are designed to minimize error between plant state and goal state despite the fact that executing actions that are predicted to result in smaller errors only can mislead to non-goal states. We develop an adaptive
Agus Naba
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Preventive Medicine ReportsObjective: Co-vaccination, or receiving multiple vaccines at once, may improve vaccination uptake and reduce missed opportunities to vaccinate. Although generally considered safe and effective, co-vaccination is not well accepted outside of travel and ...
Emily Frith +3 more
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A survey of benchmarks for reinforcement learning algorithms
South African Computer Journal, 2020 Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly.
Belinda Stapelberg, Katherine Mary Malan
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Reinforcement Learning Approaches in Social Robotics
Sensors, 2021 This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior.
Neziha Akalin, Amy Loutfi
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Humanities & Social Sciences CommunicationsA digital health intervention (DHI) using SMS precision nudging to drive RSV vaccine uptake among adults over 60 was launched with a large community pharmacy chain in 2023, two months after the vaccine’s FDA approval for adult administration in the ...
E. Susanne Blazek +5 more
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Reinforcement Learning: Theory and Applications in HEMS
Energies, 2022 The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article.
Omar Al-Ani, Sanjoy Das
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Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source