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Abstract
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning. We introduce an automated Bayesian inference framework, called AutoBayes, that explores different graphical models linking classifier, encoder, decoder, estimator and adversarial network blocks to optimize nuisance-invariant machine learning pipelines. Auto Bayes also enables learning disentangled representations, where the latent variable is split into multiple pieces to impose various relationships with the nuisance variation and task labels. We benchmark the framework on several public datasets, and provide analysis of its capability for subject-transfer learning with/without variational modeling and adversarial training. We demonstrate a significant performance improvement with ensemble learning across explored graphical models.
Highlights
The great advancement of deep learning techniques based on deep neural networks (DNN) has enabled more practical design of human-machine interfaces (HMI) through the analysis of the user’s physiological data (Faust et al, 2018), such as electroencephalogram (EEG) (Lawhern et al, 2018) and electromyogram (EMG) (Atzori et al, 2016)
We proposed a new concept called AutoBayes which explores various different Bayesian graph models to facilitate searching for the best inference strategy, suited for nuisance-robust deep learning
As a proofof-concept analysis, we demonstrated the benefit of AutoBayes for various public datasets
Summary
The great advancement of deep learning techniques based on deep neural networks (DNN) has enabled more practical design of human-machine interfaces (HMI) through the analysis of the user’s physiological data (Faust et al, 2018), such as electroencephalogram (EEG) (Lawhern et al, 2018) and electromyogram (EMG) (Atzori et al, 2016). Such biosignals are highly prone to variation depending on the biological states of each subject (Christoforou et al, 2010). Without proper reasoning, most of the search space for link connectivity will be pointless
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