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
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization—0
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)—0
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach—0
META-ANOVA: Screening interactions for interpretable machine learning—0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower—0
Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans—0
YASENN: Explaining Neural Networks via Partitioning Activation Sequences—0
Explaining Kernel Clustering via Decision Trees—0
Explaining Recurrent Neural Network Predictions in Sentiment Analysis—0
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability—0
Explanation as a process: user-centric construction of multi-level and multi-modal explanations—0
Explanations for Automatic Speech Recognition—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
Extract Local Inference Chains of Deep Neural Nets—0
Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs—0
A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset—0
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
Data-driven model reconstruction for nonlinear wave dynamics—0
Rethinking Interpretability in the Era of Large Language Models—0
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping—0
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning—0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
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Benchmark Results

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