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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 201–225 of 537 papers

TitleStatusHype
Rethinking Interpretability in the Era of Large Language Models—0
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic RegressionCode0
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?Code0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
X Hacking: The Threat of Misguided AutoMLCode0
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition—0
Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource SettingsCode0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
A Maritime Industry Experience for Vessel Operational Anomaly Detection: Utilizing Deep Learning Augmented with Lightweight Interpretable Models—0
Perceptual Musical Features for Interpretable Audio TaggingCode0
Ensemble Interpretation: A Unified Method for Interpretable Machine Learning—0
GFN-SR: Symbolic Regression with Generative Flow NetworksCode0
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics—0
Modelling wildland fire burn severity in California using a spatial Super Learner approachCode0
Neural Network Pruning by Gradient DescentCode0
LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtypeCode0
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review—0
An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City—0
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods In Psychiatry Detection Applications, Specifically Depression Disorder: A Brief Review.—0
An interpretable clustering approach to safety climate analysis: examining driver group distinction in safety climate perceptionsCode0
Hidden Citations Obscure True Impact in Science—0
Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case StudyCode0
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning—0
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning—0
Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissions—0
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Benchmark Results

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