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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 101–125 of 537 papers

TitleStatusHype
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins—0
A Semiparametric Approach to Interpretable Machine Learning—0
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions—0
A Scalable Inference Method For Large Dynamic Economic Systems—0
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Explainable AI using expressive Boolean formulas—0
Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More—0
Strategizing University Rank Improvement using Interpretable Machine Learning and Data Visualization—0
Data-driven Approach for Static Hedging of Exchange Traded Options—0
Data-driven model reconstruction for nonlinear wave dynamics—0
Data Model Design for Explainable Machine Learning-based Electricity Applications—0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
Deducing neighborhoods of classes from a fitted model—0
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life—0
A Learning Theoretic Perspective on Local Explainability—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach—0
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models—0
Expert Study on Interpretable Machine Learning Models with Missing Data—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
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Benchmark Results

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