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 281–290 of 537 papers

TitleStatusHype
Leveraging advances in machine learning for the robust classification and interpretation of networks—0
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style—0
Interpretable Predictive Maintenance for Hard Drives—0
Interpretable Reinforcement Learning with Ensemble Methods—0
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders—0
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems—0
Interpretable Two-level Boolean Rule Learning for Classification—0
Interpreting a Machine Learning Model for Detecting Gravitational Waves—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis—0
Show:102550
← PrevPage 29 of 54Next →

Benchmark Results

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