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Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

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Showing 471–480 of 537 papers

TitleStatusHype
Interpretable Convolutional Neural Networks for Preterm Birth Classification—0
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of SepsisCode0
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach—0
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networksCode0
The Partial Response Network: a neural network nomogram—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch NormalizationCode0
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Model Bridging: Connection between Simulation Model and Neural Network—0
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified