SOTAVerified

Feature Importance

Papers

Showing 76100 of 890 papers

TitleStatusHype
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate PredictionCode1
CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous DrivingCode1
Group-level Brain Decoding with Deep LearningCode1
Calibrated Explanations: with Uncertainty Information and CounterfactualsCode1
Calibrated Explanations for RegressionCode1
FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and ClassificationCode1
Hierarchical interpretations for neural network predictionsCode1
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity NormalizationCode1
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation VectorsCode1
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature ImportanceCode1
Interpretable machine learning: definitions, methods, and applicationsCode1
Label-Free Explainability for Unsupervised ModelsCode1
Learning to Faithfully Rationalize by ConstructionCode1
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetCode1
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanationsCode1
Compressing Features for Learning with Noisy LabelsCode1
Multi-View Adaptive Fusion Network for 3D Object DetectionCode1
All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path AggregationCode1
Concept Activation Regions: A Generalized Framework For Concept-Based ExplanationsCode1
ControlBurn: Feature Selection by Sparse ForestsCode1
Counterfactual Shapley Additive ExplanationsCode1
Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional BenchmarkCode1
Physics Inspired Hybrid Attention for SAR Target RecognitionCode1
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic SegmentationCode1
Understanding Information Processing in Human Brain by Interpreting Machine Learning ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Garson Variable ImportancePearson Correlation0.76Unverified
2VarImpVIANNPearson Correlation0.76Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.6Unverified
2Garson Variable ImportancePearson Correlation0.22Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.86Unverified
2Garson Variable ImportancePearson Correlation0.64Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.83Unverified
2Garson Variable ImportancePearson Correlation0.6Unverified
#ModelMetricClaimedVerifiedStatus
1VarImpVIANNPearson Correlation0.9Unverified
2Garson Variable ImportancePearson Correlation0.73Unverified
#ModelMetricClaimedVerifiedStatus
1Garson Variable ImportancePearson Correlation0.74Unverified
2VarImpVIANNPearson Correlation0.41Unverified