14 citations · 14 across the 1 of their papers we have counts for
3 papers · 1 filter
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
Pascal Sturmfels, Jesse Vig, Ali Madani +1
For protein sequence datasets, unlabeled data has greatly outpaced labeled data due to the high cost of wet-lab characterization. Recent deep-learning approaches to protein predict…
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks
Joseph D. Janizek, Pascal Sturmfels, Su-In Lee
Recent work has shown great promise in explaining neural network behavior. In particular, feature attribution methods explain which features were most important to a model's predic…
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels +2
Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whos…