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

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Showing 441–450 of 537 papers

TitleStatusHype
[Re] Explaining Groups of Points in Low-Dimensional RepresentationsCode0
[Re] Explaining Groups of Points in Low-Dimensional RepresentationsCode0
Explaining Groups of Points in Low-Dimensional RepresentationsCode0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
Signed iterative random forests to identify enhancer-associated transcription factor bindingCode0
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning ModelsCode0
Explaining a black-box using Deep Variational Information Bottleneck ApproachCode0
Neural Network Pruning by Gradient DescentCode0
Challenging common interpretability assumptions in feature attribution explanationsCode0
Regularizing Black-box Models for Improved InterpretabilityCode0
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Benchmark Results

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