Results 41 to 50 of about 295,561 (260)
Mind2Mind: Transfer Learning for GANs [PDF]
, 2021 Training generative adversarial networks (GANs) on high quality (HQ) images involves important computing resources. This requirement represents a bottleneck for the development of applications of GANs. We propose a transfer learning technique for GANs that significantly reduces training time.
Yaël Frégier, Jean-Baptiste Gouray
openaire +2 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
Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey
Sensors, 2022 Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions.
Lauren J. Wong, Alan J. Michaels
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 +4 more
wiley +1 more source
Transferable learning on analog hardware
Science Advances, 2023 While analog neural network (NN) accelerators promise massive energy and time savings, an important challenge is to make them robust to static fabrication error. Present-day training methods for programmable photonic interferometer circuits, a leading analog NN platform, do not produce networks that perform well in the presence of static hardware ...
Sri Krishna Vadlamani +2 more
openaire +3 more sources
Modulation of Homer1 EVH1 domain internal dynamics by putative autism‐associated mutations
FEBS Letters, EarlyView.The putative autism‐associated M65I and S97L variants of the EVH1 domain of the postsynaptic scaffold protein Homer1 do not exhibit substantial changes in their overall structure or partner binding. Both of them, but especially the M65I variant, show altered internal dynamics relative to the wild‐type domain on the μs‐ms timescale, indicated by the ...
Fanni Farkas +6 more
wiley +1 more source
A survey of transfer learning [PDF]
Journal of Big Data, 2016 Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same.
Karl R. Weiss +2 more
openaire +1 more source
A survey on heterogeneous transfer learning
Journal of Big Data, 2017 Transfer learning has been demonstrated to be effective for many real-world applications as it exploits knowledge present in labeled training data from a source domain to enhance a model’s performance in a target domain, which has little or no labeled ...
Oscar Day, Taghi M. Khoshgoftaar
doaj +1 more source
Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images
Diagnostics, 2022 Chest X-ray (CXR) is widely used to diagnose conditions affecting the chest, its contents, and its nearby structures. In this study, we used a private data set containing 1630 CXR images with disease labels; most of the images were disease-free, but the ...
Guan-Hua Huang +5 more
doaj +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source