2.2k citations · 2.6k across the 7 of their papers we have counts for
7 papers
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W. James Murdoch +1
For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too of…
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
Summer Devlin, Chandan Singh, W. James Murdoch +1
Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains. Howe…
Interpretable machine learning: definitions, methods, and applications
W. James Murdoch, Chandan Singh, Karl Kumbier +2
Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for pre…
Hierarchical interpretations for neural network predictions
Chandan Singh, W. James Murdoch, Bin Yu
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inabilit…
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
W. James Murdoch, Peter J. Liu, Bin Yu
The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationshi…
Automatic Rule Extraction from Long Short Term Memory Networks
W. James Murdoch, Arthur Szlam
Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a…
Expanded Alternating Optimization of Nonconvex Functions with Applications to Matrix Factorization and Penalized Regression
W. James Murdoch, Mu Zhu
We propose a general technique for improving alternating optimization (AO) of nonconvex functions. Starting from the solution given by AO, we conduct another sequence of searches o…