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 251–300 of 537 papers

TitleStatusHype
Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector—0
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons—0
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction—0
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation—0
Review of Interpretable Machine Learning Models for Disease Prognosis—0
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis—0
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities—0
Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis—0
Interpretable Machine Learning for Kronecker Coefficients—0
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study—0
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations—0
Interpretable machine-learning for predicting molecular weight of PLA based on artificial bee colony optimization algorithm and adaptive neurofuzzy inference system—0
Interpretable Machine Learning for Privacy-Preserving Pervasive Systems—0
Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage—0
Brain Age from the Electroencephalogram of Sleep—0
Interpretable Machine Learning for Self-Service High-Risk Decision-Making—0
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations—0
Beyond Model Interpretability: Socio-Structural Explanations in Machine Learning—0
Interpretable Machine Learning for Weather and Climate Prediction: A Survey—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
Interpretable machine learning-guided design of Fe-based soft magnetic alloys—0
Interpretable machine learning in Physics—0
Interpretable Machine Learning in Physics: A Review—0
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation—0
Interpretable machine learning models: a physics-based view—0
Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems—0
Interpretable Machine Learning Models for the Digital Clock Drawing Test—0
Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach—0
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development—0
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs—0
Leveraging advances in machine learning for the robust classification and interpretation of networks—0
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style—0
Interpretable Predictive Maintenance for Hard Drives—0
Interpretable Reinforcement Learning with Ensemble Methods—0
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders—0
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems—0
Interpretable Two-level Boolean Rule Learning for Classification—0
Interpreting a Machine Learning Model for Detecting Gravitational Waves—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers—0
Interpreting Neural Ranking Models using Grad-CAM—0
Exploring Interpretability for Predictive Process Analytics—0
Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning—0
Automated Learning of Interpretable Models with Quantified Uncertainty—0
Is Grad-CAM Explainable in Medical Images?—0
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity—0
Show:102550
← PrevPage 6 of 11Next →

Benchmark Results

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