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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 126–150 of 537 papers

TitleStatusHype
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
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
Perceptual Musical Features for Interpretable Audio TaggingCode0
Ensemble Interpretation: A Unified Method for Interpretable Machine Learning—0
Generative Inverse Design of Metamaterials with Functional Responses by Interpretable LearningCode1
GFN-SR: Symbolic Regression with Generative Flow NetworksCode0
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image RecognitionCode1
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
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
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
ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning—0
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Benchmark Results

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