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Found 4 papers, 3 papers with code
Variational Mixture of HyperGenerators for Learning Distributions Over Functions
Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces.
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
Our experiments show that HH-VAEM outperforms existing baselines in the tasks of missing data imputation and supervised learning with missing features.
Unsupervised Learning of Global Factors in Deep Generative Models
We present a novel deep generative model based on non i. i. d.
Deep Sequential Models for Suicidal Ideation from Multiple Source Data
This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential models.
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