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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

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Showing 301–325 of 537 papers

TitleStatusHype
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning—0
Sequencing Silicates in the IRS Debris Disk Catalog I: Methodology for Unsupervised Clustering—0
Severity and Mortality Prediction Models to Triage Indian COVID-19 Patients—0
Shapley variable importance cloud for machine learning models—0
Show or Suppress? Managing Input Uncertainty in Machine Learning Model Explanations—0
SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis—0
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity—0
Structural Node Embeddings with Homomorphism Counts—0
Subgroup Analysis via Model-based Rule Forest—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
Taming Waves: A Physically-Interpretable Machine Learning Framework for Realizable Control of Wave Dynamics—0
Techniques for Interpretable Machine Learning—0
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification—0
Tensor Polynomial Additive Model—0
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks—0
The Doctor Just Won't Accept That!—0
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR—0
The Most Important Features in Generalized Additive Models Might Be Groups of Features—0
The Partial Response Network: a neural network nomogram—0
The Promise and Peril of Human Evaluation for Model Interpretability—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
The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review—0
Topological data analysis of zebrafish patterns—0
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications—0
Toward More Generalized Malicious URL Detection Models—0
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Benchmark Results

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