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 226–250 of 537 papers

TitleStatusHype
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Automated Learning of Interpretable Models with Quantified Uncertainty—0
Interpretable Convolutional Neural Networks for Preterm Birth Classification—0
Interpretable and Explainable Machine Learning for Materials Science and Chemistry—0
Interpretability with full complexity by constraining feature information—0
Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
Interpretability of machine learning based prediction models in healthcare—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Deducing neighborhoods of classes from a fitted model—0
Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing—0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models—0
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus—0
Integrating White and Black Box Techniques for Interpretable Machine Learning—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—0
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Benchmark Results

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