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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 451–500 of 537 papers

TitleStatusHype
Efficient Learning of Interpretable Classification Rules—0
A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques—0
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition—0
Deducing neighborhoods of classes from a fitted model—0
Enriched Annotations for Tumor Attribute Classification from Pathology Reports with Limited Labeled Data—0
Ensemble Interpretation: A Unified Method for Interpretable Machine Learning—0
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning—0
Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning—0
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
ExMo: Explainable AI Model using Inverse Frequency Decision Rules—0
Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity—0
Expert Study on Interpretable Machine Learning Models with Missing Data—0
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
Explainable AI Enabled Inspection of Business Process Prediction Models—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Explainable AI using expressive Boolean formulas—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Tribe or Not? Critical Inspection of Group Differences Using TribalGram—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—0
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins—0
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