WebNov 26, 2024 · Yeah, probably it doesn't matter since you initialize inv_std so that the softplus puts it at 1. Maybe its slightly easier to get a singular distribution (i.e. close to zero variance) with the covariance parameterization, don't think it should be too bad though :) 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
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WebA BSTRACT We propose Flow Gaussian Mixture Model (FlowGMM), a general-purpose method for semi-supervised learning based on a simple and principled proba-bilistic framework. We approximate the joint distribution of the labeled and un-labeled data with a flexible mixture model implemented as a Gaussian mixture transformed by a normalizing … WebWe propose FlowGMM, an end-to-end approach to generative semi supervised learning with normalizing flows, using a latent Gaussian mixture model. FlowGMM i... bil otitis externa icd 10
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