Ensemble deep learning in bioinformatics

By Yue Cao, Thomas A. Geddes, Jean Yee Hwa Yang, Pengyi Yang. Nature Machine Intelligence, 2020

303 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4050c80ba36c6f86b0684a59be70182557b0770c

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Citations in the corpus

YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors (2022, methodology): “Model re-parametrization techniques [71, 31, 75, 19, 33, 11, 4, 24, 13, 12, 10, 29, 14, 78] merge multiple computational modules into one at inference stage.”
A Survey on ensemble learning under the era of deep learning (2021, background): “2020) that mostly discussed about traditional ensemble learning, reviews (Cao et al. 2020) that particularly discussed ensemble deep learning in bioinformatics, or specific technical innovations with ensemble learning introduced such as GAN-Ensembles (Durugkar et al.”
Physics-informed machine learning: case studies for weather and climate modelling (2021, methodology): “How can uncertainty be quantified in spatio-temporal forecasting? Although UQ has as yet not been employed in the PIML models for spatio-temporal forecasting discussed above, ensemble ML techniques provide opportunities for characterizing and estimating uncertainties [126].”
Ensemble deep learning: A review (2021, background): “Explicit/implicit produce ensembles out of a single network at the expense of base model diversity [25] as the lower level features across the models are likely to be the same.”
A survey of uncertainty in deep neural networks (2021, background): “methods to different kinds of practical tasks and approaches, as for example bioinformatics [219], [220], [221], remote sensing [222], [223], [224], or reinforcement learning [225], [226] can be found in the literature.”