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 426–450 of 537 papers

TitleStatusHype
Towards Analogy-Based Explanations in Machine Learning—0
Interpreting Neural Ranking Models using Grad-CAM—0
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism PredictionCode1
Interpretable Learning-to-Rank with Generalized Additive Models—0
Explaining How Deep Neural Networks Forget by Deep VisualizationCode0
Offensive Language Detection ExplainedCode0
Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin LiquidsCode0
Neural Additive Models: Interpretable Machine Learning with Neural NetsCode1
Adversarial Attacks and Defenses: An Interpretation Perspective—0
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning—0
Understanding the decisions of CNNs: An in-model approachCode1
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic modelsCode0
BreastScreening: On the Use of Multi-Modality in Medical Imaging DiagnosisCode1
Ontology-based Interpretable Machine Learning for Textual DataCode0
Born-Again Tree EnsemblesCode1
Interpretable machine learning models: a physics-based view—0
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications—0
Explaining Groups of Points in Low-Dimensional RepresentationsCode0
Interpretability of machine learning based prediction models in healthcare—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—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
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Show:102550
← PrevPage 18 of 22Next →

Benchmark Results

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