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Found 4 papers, 1 papers with code
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions.
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions.
A Minimalistic Approach to Sum-Product Network Learning for Real Applications
Sum-Product Networks (SPNs) are a class of expressive yet tractable hierarchical graphical models.
Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests
In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task.
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