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doi: 10.1016/j.ibmb.2019.04.002 163 ZhangWTettamantiGBassalTHeryantoCEleftherianosIMohamedA
This model explained about 19 % of the MOTS-c variance (adjusted R 2 = 0.1877)
& Gould, A
In Deep Learning: Practice and Trends (NIPS 2017)[2], prominent researchers offered a simple abstraction that virtually all deep learning approaches can be characterised as either augmenting architectures or loss functions, or applying the previous to new input/output combinations
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