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 201–210 of 537 papers

TitleStatusHype
Explanations for Automatic Speech Recognition—0
Structural Neural Additive Models: Enhanced Interpretable Machine LearningCode1
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—0
Data-driven Approach for Static Hedging of Exchange Traded Options—0
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis—0
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
Learning Support and Trivial Prototypes for Interpretable Image ClassificationCode1
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
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Benchmark Results

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