Results 71 to 80 of about 199,039 (272)

Navigating Ternary Doping in Li‐ion Cathodes With Closed‐Loop Multi‐Objective Bayesian Optimization

Advanced Materials, EarlyView.
The search for advanced battery materials is pushing us into highly complex composition spaces. Here, a space with about 14 million unique combinations is efficiently explored using high‐throughput experimentation guided by Bayesian optimization with a deep kernel trained on both the Materials Project database and our data.
Nooshin Zeinali Galabi, Cheng‐Hao Liu, Moksh Jain, Marc Kamel, Shipeng Jia, Yoshua Bengio, Eric McCalla   +6 more
wiley   +1 more source

Modeling of moral decisions with deep learning

Visual Computing for Industry, Biomedicine, and Art, 2020
One example of an artificial intelligence ethical dilemma is the autonomous vehicle situation presented by Massachusetts Institute of Technology researchers in the Moral Machine Experiment.
Christopher Wiedeman, Ge Wang, Uwe Kruger   +2 more
doaj   +1 more source

Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program

The Plant Genome, 2021
Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine‐ and deep‐learning algorithms applied to complex traits in plants can improve prediction accuracies.
Karansher Sandhu, Shruti Sunil Patil, Michael Pumphrey, Arron Carter   +3 more
doaj   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

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

Deep active learning for multi label text classification

Scientific Reports
Given a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant labels for a text. Recently, deep learning models get inspiring results in MLTC.
Qunbo Wang, Hangu Zhang, Wentao Zhang, Lin Dai, Yu Liang, Haobin Shi   +5 more
doaj   +1 more source

Federated Deep Learning with Bayesian Privacy

, 2021
Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with billions of model parameters, existing privacy-preserving solutions are unsatisfactory.
Gu, Hanlin, Fan, Lixin, Li, Bowen, Kang, Yan, Yao, Yuan, Yang, Qiang   +5 more
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

Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning

Applied Sciences
The prediction of blast loading for complex structures using deep learning requires extensive training data from field experiments or numerical simulations.
Meilin Pan, Weiwen Peng, Chunjiang Leng, Jiulu Qiu, Wei Zhong   +4 more
doaj   +1 more source

Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

IJAIN (International Journal of Advances in Intelligent Informatics), 2019
Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for ...
Tessy Badriyah, Dimas Bagus Santoso, Iwan Syarif, Daisy Rahmania Syarif   +3 more
doaj   +1 more source

Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic.

PLoS ONE, 2021
Recently, there has been much attention in the use of machine learning methods, particularly deep learning for stock price prediction. A major limitation of conventional deep learning is uncertainty quantification in predictions which affect investor ...
Rohitash Chandra, Yixuan He
doaj   +1 more source