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Found 6 papers, 5 papers with code
Inference and Learning for Generative Capsule Models
In this paper we specify a generative model for such data, and derive a variational algorithm for inferring the transformation of each model object in a scene, and the assignments of observed parts to the objects.
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs
Seeing as a systematic outlier is a combination of patterns of a clean instance and systematic error patterns, our main insight is that inliers can be modelled by a smaller representation (subspace) in a model than outliers.
Inference for Generative Capsule Models
Capsule networks (see e. g. Hinton et al., 2018) aim to encode knowledge and reason about the relationship between an object and its parts.
VAEs in the Presence of Missing Data
Real world datasets often contain entries with missing elements e. g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests.
Wrangling Messy CSV Files by Detecting Row and Type Patterns
Existing dialect detection approaches are few and non-robust.
Databases E.5; H.2.8
Handling Incomplete Heterogeneous Data using VAEs
Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data.
