56 citations · 59 across the 3 of their papers we have counts for
4 papers
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
Viktoriya Krakovna, Finale Doshi-Velez
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, a…
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models
Viktoriya Krakovna, Finale Doshi-Velez
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, a…
A Minimalistic Approach to Sum-Product Network Learning for Real Applications
Viktoriya Krakovna, Moshe Looks
Sum-Product Networks (SPNs) are a class of expressive yet tractable hierarchical graphical models. LearnSPN is a structure learning algorithm for SPNs that uses hierarchical co-clu…
Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests
Viktoriya Krakovna, Jiong Du, Jun S. Liu
It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest…