Results 71 to 80 of about 827,210 (276)

Deep Neural Network Ensembles [PDF]

, 2019
Current deep neural networks suffer from two problems; first, they are hard to interpret, and second, they suffer from overfitting. There have been many attempts to define interpretability in neural networks, but they typically lack causality or generality.
openaire   +2 more sources

Diversity and complexity in neural organoids

FEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Efficient Deep Feature Learning and Extraction via StochasticNets

, 2015
Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data.
Fieguth, Paul, Shafiee, Mohammad Javad, Siva, Parthipan, Wong, Alexander   +3 more
core   +1 more source

Linking neurogenesis, oligodendrogenesis, and myelination defects to neurodevelopmental disruption in primary mitochondrial disorders

FEBS Letters, EarlyView.
Mitochondrial remodeling shapes neural and glial lineage progression by matching metabolic supply with demand. Elevated OXPHOS supports differentiation and myelin formation, while myelin compaction lowers mitochondrial dependence, revealing mitochondria as key drivers of developmental energy adaptation.
Sahitya Ranjan Biswas, Porter L. Tomsick, Alicia M. Pickrell, Paul D. Morton   +3 more
wiley   +1 more source

The Construction of Smart Chinese Medicine Cloud Health Platform Based on Deep Neural Networks

International Transactions on Electrical Energy Systems, 2022
In order to improve the efficiency of doctors’ diagnosis and treatment, the state has built a Chinese medicine cloud health platform. However, most medical institutions currently use internal networks, and the technical standards and specifications are ...
Yaofeng Miao, Yuan Zhou
doaj   +1 more source

Non-attracting Regions of Local Minima in Deep and Wide Neural Networks

, 2020
Understanding the loss surface of neural networks is essential for the design of models with predictable performance and their success in applications.
Petzka, Henning, Sminchisescu, Cristian
core  

Deep Learning for Forecasting Stock Returns in the Cross-Section

, 2018
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition ...
A Subrahmanyam, AJ Smola, C Krauss, CR Harvey, D Olson, GS Atsalakis, I Goodfellow, L Breiman, L Kryzanowski, Q Cao, RD McLean, S Soni, Y LeCun   +12 more
core   +1 more source

Deep learning in spiking neural networks [PDF]

Neural Networks, 2019
In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
Kheradpisheh, Saeed Reza, Tavanaei, Amirhossein, Ghodrati, Masoud, Kheradpisheh, Saeed Reza, Masquelier, Timothée, Maida, Anthony   +5 more
openaire   +3 more sources

Epigenetic blind spots – the role of DNA methylation dynamics in stem cell‐based models of embryogenesis

FEBS Letters, EarlyView.
Embryo‐like structures (stembryos) are an innovative tool, but they are hindered by experimental variability and limited developmental potential. DNA methylation is crucial for mammalian development, but its status in stembryo models is poorly characterized.
Sara Canil, Anna Corrà, Annalisa Scapolatiello, Chengxiang Qiu, Gianluca Amadei   +4 more
wiley   +1 more source

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Nature Communications
Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models.
Zhenjia Chen, Zhenyuan Lin, Ji Yang, Cong Chen, Di Liu, Liuting Shan, Yuanyuan Hu, Tailiang Guo, Huipeng Chen   +8 more
doaj   +1 more source