repoGitHubTrust 82 · PrimaryPublished 3d agoLive · 3d ago
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 56%Understanding Annotator Safety Policy with Interpretability - Apple Machine Learning Research →
- PossiblePossibly related (embedding) · 53%Surrogate Fidelity: When Can Open LLMs Explain Closed Ones? →
- PossiblePossibly related (embedding) · 30%The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology →
“Possibly related via embedding similarity 0.58 (not asserted). Timestamp check: artifact after paper (+12d).”
- PossiblePossibly related (embedding) · 29%Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence →
“Possibly related via embedding similarity 0.57 (not asserted). Timestamp check: artifact after paper (+4d).”
- PossiblePossibly related (embedding) · 28%ExplAIner: A Declarative Query Language for Explaining Classification Models →
“Possibly related via embedding similarity 0.55 (not asserted). Timestamp check: artifact after paper (+6d).”
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Related to (incoming)
Related across the graph
paperSteering Neural Network Training through Interpretable Constraints Based on Partial DependencepaperExplAIner: A Declarative Query Language for Explaining Classification ModelsnewsUnderstanding Annotator Safety Policy with Interpretability - Apple Machine Learning ResearchpaperSurrogate Fidelity: When Can Open LLMs Explain Closed Ones?paperThe Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology