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 451–475 of 537 papers

TitleStatusHype
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
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
Ontology-based Interpretable Machine Learning for Textual DataCode0
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
Exploring Interpretability for Predictive Process Analytics—0
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
A Decision-Theoretic Approach for Model Interpretability in Bayesian FrameworkCode0
Topological data analysis of zebrafish patterns—0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
MonoNet: Towards Interpretable Models by Learning Monotonic Features—0
Interpretable Convolutional Neural Networks for Preterm Birth Classification—0
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of SepsisCode0
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach—0
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networksCode0
The Partial Response Network: a neural network nomogram—0
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Benchmark Results

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