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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 537 papers

TitleStatusHype
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
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network RobustnessCode0
A hybrid machine learning framework for analyzing human decision making through learning preferences—0
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)—0
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networksCode0
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model—0
Full-Gradient Representation for Neural Network VisualizationCode0
Visualization of Convolutional Neural Networks for Monocular Depth EstimationCode0
Open Issues in Combating Fake News: Interpretability as an Opportunity—0
Re-Ranking Words to Improve Interpretability of Automatically Generated TopicsCode0
Explaining a black-box using Deep Variational Information Bottleneck ApproachCode0
Regularizing Black-box Models for Improved InterpretabilityCode0
ProtoAttend: Attention-Based Prototypical LearningCode0
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach—0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
Comparative Document Summarisation via ClassificationCode0
MLIC: A MaxSAT-Based framework for learning interpretable classification rulesCode0
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models InsteadCode0
YASENN: Explaining Neural Networks via Partitioning Activation Sequences—0
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style—0
Towards making NLG a voice for interpretable Machine Learning—0
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Benchmark Results

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