Results 261 to 270 of about 1,532,152 (301)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Sim-to-real domain adaptation based completion level recognition for autonomous micro-drilling in biomedical application. [PDF]

Sci Rep
Zhao E, Perez SAH, Harada K.
europepmc   +1 more source

Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance. [PDF]

Eur Radiol Exp
Vaccaro M, Rosa E, Placidi E, Guarnera A, Secinaro A, Gandolfo C, Garganese MC, Napolitano A.   +7 more
europepmc   +1 more source

Artificial Neural Networks for Discrimination of Automotive Clear Coats by Vehicle Manufacturer. [PDF]

Sensors (Basel)
Lavine BK, White CG, Heisterkamp DR.
europepmc   +1 more source

Leveraging large language models for gastrointestinal injury detection in athletes: a medical image analysis approach. [PDF]

Sci Rep
Luo S, Xie J, Du Y, Wu W, Tong J.
europepmc   +1 more source

An Ensemble Learning for Automatic Stroke Lesion Segmentation Using Compressive Sensing and Multi-Resolution U-Net. [PDF]

Biomimetics (Basel)
Emami M, Tinati MA, Musevi Niya J, Danishvar S.   +3 more
europepmc   +1 more source

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data. [PDF]

Health Data Sci
Sun C, Song M, Cai D, Zhang B, Li H, Hong S.   +5 more
europepmc   +1 more source

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SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks

IEEE Transactions on Neural Networks and Learning Systems, 2020
Group convolution is widely used in many mobile networks to remove the filter's redundancy from the channel extent. In order to further reduce the redundancy of group convolution, this article proposes a novel repeated group convolutional (RGC) kernel, which has M primary groups, and each primary group includes N tiny groups.
Jinxing Li, Yao Lu, David Zhang
exaly   +3 more sources

KNOWLEDGE TRANSFER IN DEEP CONVOLUTIONAL NEURAL NETS

International Journal on Artificial Intelligence Tools, 2008
Knowledge transfer is widely held to be a primary mechanism that enables humans to quickly learn new complex concepts when given only small training sets. In this paper, we apply knowledge transfer to deep convolutional neural nets, which we argue are particularly well suited for knowledge transfer. Our initial results demonstrate that components of a
Steven Gutstein, Olac Fuentes, Eric Freudenthal   +2 more
openaire   +3 more sources