Results 61 to 70 of about 224,776 (267)
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
Stochastic Control for Bayesian Neural Network Training
Entropy, 2022 In this paper, we propose to leverage the Bayesian uncertainty information encoded in parameter distributions to inform the learning procedure for Bayesian models.
Ludwig Winkler +2 more
doaj +1 more source
, 2015 We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e.g., for applications involving ...
Korattikara, Anoop +3 more
core +1 more source
Bayesian Neural Networks: Essentials
, 2021 Bayesian neural networks utilize probabilistic layers that capture uncertainty over weights and activations, and are trained using Bayesian inference. Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way to extend conventional deep neural ...
openaire +2 more sources
Hydrogel‐Based Functional Materials: Classifications, Properties, and Applications
Advanced Materials Technologies, EarlyView.Conductive hydrogels have emerged as promising materials for smart wearable devices due to their outstanding flexibility, multifunctionality, and biocompatibility. This review systematically summarizes recent progress in their design strategies, focusing on monomer systems and conductive components, and highlights key multifunctional properties such as
Zeyu Zhang, Zao Cheng, Patrizio Raffa
wiley +1 more source
Weight Priors for Learning Identity Relations [PDF]
, 2019 Learning abstract and systematic relations has been an open issue in neural network learning for over 30 years. It has been shown recently that neural networks do not learn relations based on identity and are unable to generalize well to unseen data. The
Kopparti, R. M., Weyde, T.
core
Reducing Personalization Time and Energy Cost While Walking Outdoors with a Portable Exosuit
Advanced Robotics Research, EarlyView.Rapid Real‐World Optimization! An AF‐based human‐in‐the‐loop optimization strategy rapidly personalizes a portable hip extension exosuit for incline walking. Real‐time Bayesian optimization of assistive force significantly reduces metabolic energy—up to 16.2%—while converging in just 3 min 24 s.
Kimoon Nam +7 more
wiley +1 more source
Bayesian Perceptron: Towards fully Bayesian Neural Networks [PDF]
2020 59th IEEE Conference on Decision and Control (CDC), 2020 Accepted for publication at the 59th IEEE Conference on Decision and Control (CDC) 2020.
openaire +2 more sources
Flexible Sensor‐Based Human–Machine Interfaces with AI Integration for Medical Robotics
Advanced Robotics Research, EarlyView.This review explores how flexible sensing technology and artificial intelligence (AI) significantly enhance human–machine interfaces in medical robotics. It highlights key sensing mechanisms, AI‐driven advancements, and applications in prosthetics, exoskeletons, and surgical robotics.
Yuxiao Wang +5 more
wiley +1 more source
Advanced Robotics Research, EarlyView.An introduction for multidrive and environment‐adaptive micro/nanorobotics: design and fabrication strategies, intelligent actuation, and their applications. Various intelligent actuation approaches—magnetic, acoustic, optical, chemical, and biological—can be synergistically designed to enhance flexibility and adaptive behavior for precision medicine ...
Aiqing Ma +10 more
wiley +1 more source