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 51–100 of 537 papers

TitleStatusHype
Optimal Counterfactual Explanations in Tree EnsemblesCode1
DISSECT: Disentangled Simultaneous Explanations via Concept TraversalsCode1
Interpretable machine learning for high-dimensional trajectories of aging healthCode1
Do Feature Attribution Methods Correctly Attribute Features?Code1
Grouped Feature Importance and Combined Features Effect PlotCode1
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationCode1
TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualizationCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Neural Prototype Trees for Interpretable Fine-grained Image RecognitionCode1
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine LearningCode1
Interpretable Machine Learning with an Ensemble of Gradient Boosting MachinesCode1
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction TaskCode1
Interpretable Machine Learning Approaches to Prediction of Chronic HomelessnessCode1
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals PredictionCode1
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit LayersCode1
Modern Hopfield Networks and Attention for Immune Repertoire ClassificationCode1
Generalized and Scalable Optimal Sparse Decision TreesCode1
How Interpretable and Trustworthy are GAMs?Code1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism PredictionCode1
Neural Additive Models: Interpretable Machine Learning with Neural NetsCode1
Understanding the decisions of CNNs: An in-model approachCode1
BreastScreening: On the Use of Multi-Modality in Medical Imaging DiagnosisCode1
Born-Again Tree EnsemblesCode1
Understanding Deep Networks via Extremal Perturbations and Smooth MasksCode1
Improving performance of deep learning models with axiomatic attribution priors and expected gradientsCode1
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc InterpretabilityCode1
Interpretable machine learning: definitions, methods, and applicationsCode1
RISE: Randomized Input Sampling for Explanation of Black-box ModelsCode1
Hierarchical interpretations for neural network predictionsCode1
A Unified Approach to Interpreting Model PredictionsCode1
Axiomatic Attribution for Deep NetworksCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
"Why Should I Trust You?": Explaining the Predictions of Any ClassifierCode1
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
The Most Important Features in Generalized Additive Models Might Be Groups of Features—0
Leveraging Predictive Equivalence in Decision TreesCode0
Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images—0
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders—0
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics—0
An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia—0
midr: Learning from Black-Box Models by Maximum Interpretation DecompositionCode0
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study—0
Are machine learning interpretations reliable? A stability study on global interpretations—0
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients—0
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy—0
Show:102550
← PrevPage 2 of 11Next →

Benchmark Results

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