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Found 5 papers, 2 papers with code
Compressed Sensing of Generative Sparse-latent (GSL) Signals
We consider reconstruction of an ambient signal in a compressed sensing (CS) setup where the ambient signal has a neural network based generative model.
DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup
DANSE provides a closed-form posterior of the state of the model-free process, given linear measurements of the state.
DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models
Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem.
Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with Explainability
For a standard speech phone classification setup involving 39 phones (classes) and the TIMIT dataset, we show that the use of standard features called mel-frequency-cepstral-coeffcients (MFCCs), the proposed generative models, and the decision fusion together can achieve $86. 6\%$ accuracy by generative training only.
Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows
We test the robustness of a maximum-likelihood (ML) based classifier where sequential data as observation is corrupted by noise.
