Results 81 to 90 of about 9,899,096 (370)
NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants [PDF]
arXiv, 2023 Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it is still challenging to unleash tiny deep learning's full potential on both large-scale datasets and downstream tasks due to the under-fitting issues caused by the limited ...
arxiv
miRNA‐29 regulates epidermal and mesenchymal functions in skin repair
FEBS Letters, EarlyView.miRNA‐29 inhibits cell‐to‐cell and cell‐to‐matrix adhesion by silencing mRNA targets. Adhesion is controlled by complex interactions between many types of molecules coded by mRNAs. This is crucial for keeping together the layers of the skin and for regenerating the skin after wounding.
Lalitha Thiagarajan +10 more
wiley +1 more source
Deep Learning in Neuroradiology [PDF]
American Journal of Neuroradiology, 2018 Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging.
G. Zaharchuk +4 more
openaire +3 more sources
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey [PDF]
arXiv, 2021 Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision.
arxiv
Molecular Oncology, EarlyView.The number of circulating tumor cells obtained from prostate cancer patients was increased approximately 5‐fold compared to regular CellSearch when processing 2 mL diagnostic leukapheresis material aliquots and increased by 44‐fold when processing 20 mL DLA aliquots using the flow enrichment target capture Halbach‐array.
Michiel Stevens +8 more
wiley +1 more source
Encyclopedia with Semantic Computing and Robotic Intelligence, 2017 Seminar about deep learning and ...
Arjona, Gorchs, , Hernández-Ramírez
openaire +3 more sources
Engineering ReportsMachine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of ...
Jannatul Mauya +5 more
doaj +1 more source
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models [PDF]
arXiv, 2021 Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. We distinguish two approaches to probabilistic deep learning: probabilistic neural networks and deep probabilistic models.
arxiv
Molecular Oncology, EarlyView.Urine is a rich source of biomarkers for cancer detection. Tumor‐derived material is released into the bloodstream and transported to the urine. Urine can easily be collected from individuals, allowing non‐invasive cancer detection. This review discusses the rationale behind urine‐based cancer detection and its potential for cancer diagnostics ...
Birgit M. M. Wever +1 more
wiley +1 more source
, 2018 Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains ...
Stefan Lörwald +10 more
openaire +3 more sources