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Found 7 papers, 5 papers with code
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
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.
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
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.
Interpretable machine learning: definitions, methods, and applications
Official code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions" https://arxiv. org/abs/1806. 05337
Hierarchical interpretations for neural network predictions
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables.
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
On the task of sentiment analysis with the Yelp and SST data sets, we show that CD is able to reliably identify words and phrases of contrasting sentiment, and how they are combined to yield the LSTM's final prediction.
Automatic Rule Extraction from Long Short Term Memory Networks
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.
Expanded Alternating Optimization of Nonconvex Functions with Applications to Matrix Factorization and Penalized Regression
We propose a general technique for improving alternating optimization (AO) of nonconvex functions.
