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 176–200 of 537 papers

TitleStatusHype
Causal Dependence Plots—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
Extract Local Inference Chains of Deep Neural Nets—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Fast Approximation of the Shapley Values Based on Order-of-Addition Experimental Designs—0
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
Category-Specific Topological Learning of Metal-Organic Frameworks—0
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease—0
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping—0
Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data—0
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations—0
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq—0
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process—0
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning—0
Full interpretable machine learning in 2D with inline coordinates—0
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
GAMformer: In-Context Learning for Generalized Additive Models—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning—0
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance—0
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City—0
Explainable AI using expressive Boolean formulas—0
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Benchmark Results

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