Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes into developing techniques for interpreting the interactions learned by a given model, fewer studies focus on assessing the quality of such explanations. Even fewer focus on how to adjust the model to produce explanations faithful to prior knowledge, a process known as explanation-guided learning. Furthermore, most approaches in this area focus on classification problems and usually assume prior knowledge about which input features or regions
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- PossiblePossibly related (embedding) · 47%data_ingenieur/s10-machine-learning-supervise →
- PossiblePossibly related (embedding) · 47%Understanding Annotator Safety Policy with Interpretability - Apple Machine Learning Research →
- PossiblePossibly related (embedding) · 46%Inference →
- PossiblePossibly related (embedding) · 46%Explainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study - Nature →
- PossiblePossibly related (embedding) · 29%interpretml/interpret →
“Possibly related via embedding similarity 0.57 (not asserted). Timestamp check: artifact after paper (+4d).”
- LinkedLinked via arxiv author · 85%Yann Claes →
“Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence”
- LinkedLinked via arxiv author · 85%Pierre Geurts →
“Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence”
- LinkedLinked via arxiv author · 85%Vân Anh Huynh-Thu →
“Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence”