Results 61 to 70 of about 5,846,406 (312)

Deep Neural Mobile Networking

CoRR, 2020
PhD thesis, University of Edinburgh (2020)
openaire   +4 more sources

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

FEBS Letters, EarlyView.
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley   +1 more source

Sequence-discriminative training of deep neural networks [PDF]

, 2013
Sequence-discriminative training of deep neural networks (DNNs) is investigated on a 300 hour American English conversational telephone speech task. Different sequence-discriminative criteria ndash;- maximum mutual information (MMI), minimum phone error (
Ghoshal, Arnab, Veselý, Karel, Povey, Daniel, Burget, Lukás   +3 more
core  

A hybrid quantum–classical neural network with deep residual learning [PDF]

, 2021
Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept.
Zhao, Guoying, Liang, Yanying, Silvén, Olli, Zheng, Zhu-Jun, Peng, Wei   +4 more
core   +1 more source

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

Parameter-Efficient Deep Neural Networks With Bilinear Projections.

, 2020
Recent research on deep neural networks (DNNs) has primarily focused on improving the model accuracy. Given a proper deep learning framework, it is generally possible to increase the depth or layer width to achieve a higher level of accuracy.
Yu, L, Gao, Y, Yu, Litao, Zhou, Jun, Zhang, Jian, Zhou, J, Zhang, J, Gao, Yongsheng   +7 more
core   +1 more source

Copulas and deep learning: a review

Dependence Modeling
In the last two decades, there has been a surge in the research on neural networks, and particularly on deep learning. At the same time, copulas as a statistical modeling tool for multivariate distributions became more and more popular.
Coblenz Maximilian, Grothe Oliver, Liu Bolin, Weniger David   +3 more
doaj   +1 more source

The human gut microbiome across the life course

FEBS Letters, EarlyView.
Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero, Basak Bahcivanci, Antonietta Hayhoe, Animesh Acharjee, Ezgi Özkurt   +4 more
wiley   +1 more source

Robust Local Cluster Neural Networks (ESANN) [PDF]

, 2006
Eickhoff R, Sitte J, Rückert U. Robust Local Cluster Neural Networks (ESANN). In: Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN).
Eickhoff, Ralf, Rueckert, Ulrich, Sitte, Joaquin, Rückert, Ulrich   +3 more
core  

Dissecting Deep Neural Networks

CoRR, 2019
In exchange for large quantities of data and processing power, deep neural networks have yielded models that provide state of the art predication capabilities in many fields. However, a lack of strong guarantees on their behaviour have raised concerns over their use in safety-critical applications.
Haakon Robinson, Adil Rasheed, Omer San
openaire   +2 more sources