Hierarchical Multi-Label Classification Networks

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COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios (2020, methodology): “Clus-HMC was chosen because it is considered in the literature as the state-of-the-art hierarchical classiﬁcation framework [64, 65, 66].”
Machine learning for discovering missing or wrong protein function annotations (2019, methodology): “Wehrmann et al. [5] extended this idea with an extra global loss function, allowing gradients to flow across all neural networks.”
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, unknown): “Our approach is similar to previous work in robustness to noisy labels (Goldberger & Ben-Reuven, 2017) and hierarchical multi-labels flavors of classification problems (Wehrmann et al , 2018).”
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL (2020, background): “The training of hierarchical classiﬁers is a topic with a rich history in the machine learning literature, see for example [43] for a dedicated review and [44] for a recent deep learning approach to the topic.”
Retrieval And Structuring Augmented Generation with Large Language Models (2025, unknown): “CatE [164] creates a joint word embedding space, while KeyETM [72] extends embedded topic modeling [45] by integrating topical keywords.”