SOTAVerified

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
Full-Gradient Representation for Neural Network VisualizationCode0
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc InterpretabilityCode1
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
Interpretable machine learning: definitions, methods, and applicationsCode1
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
Towards making NLG a voice for interpretable Machine Learning—0
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style—0
MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry—0
Signed iterative random forests to identify enhancer-associated transcription factor bindingCode0
Interpretable Reinforcement Learning with Ensemble Methods—0
iNNvestigate neural networks!Code0
Knowledge Representation with Conceptual Spaces—0
Techniques for Interpretable Machine Learning—0
AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling—0
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems—0
Show:102550
← PrevPage 20 of 22Next →

Benchmark Results

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