WebFlowGMM (n llabels) 98.94 82.42 78.24 FlowGMM-cons (n llabels) 99.0 86.44 80.9 Uncertainty. FlowGMM produces overconfident predictions on in-domain data; this … Webture Model (FlowGMM). FlowGMM models the data as a mixture of complex distributions, im-plemented by an invertible transformation of a Gaussian mixture. This hybrid …
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WebWe propose FlowGMM, a new probabilistic classifi-cation model based on normalizing flows that can be naturally applied to semi-supervised learning. We show that FlowGMM has good performance on a broad range of semi-supervised tasks, including image, text and tabular data classification. We propose a new type of probabilistic consistency WebFlowGMM is distinct in its simplicity, unified treatment of labelled and unlabelled data with an exact likelihood, interpretability, and broad applicability beyond image data. We show … greater beulah facebook
[1912.13025] Semi-Supervised Learning with Normalizing Flows - arXiv.org
WebFlowGMM is distinct in its simplicity, unified treatment of labelled and unlabelled data with an exact likelihood, interpretability, and broad applicability beyond image data. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data, tabular data, and semi-supervised image classification. WebFlowGMM is distinct in its simplicity, unified treatment of labelled and unlabelled data with an exact likelihood, interpretability, and broad applicability beyond image data. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data, tabular data, and semi-supervised image classification. WebFlowGMM is distinct in its simplicity, unified treatment of labeled and unlabeled data with an exact likelihood, interpretability, and broad applicability beyond image data. We show … greater bham ymca