Interpretable machine learning: definitions, methods, and applications
Official code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions" https://arxiv.org/abs/1806.05337
Code (6)
Tasks
BIG-bench Machine LearningFeature ImportanceInterpretable Machine LearningSimilar Papers 제목 키워드 기반
The Definitions of Interpretability and Learning of Interpretable Models
As machine learning algorithms getting adopted in an ever-increasing number of applications, interpretation has emerged as a crucial desideratum. In this paper, we propose a mathematical definition for the human-interpre…
Decision MakingAssessing the Local Interpretability of Machine Learning Models
The increasing adoption of machine learning tools has led to calls for accountability via model interpretability. But what does it mean for a machine learning model to be interpretable by humans, and how can this be asse…
BIG-bench Machine LearningInterpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis
We propose using automatically generated natural language definitions of contextualised word usages as interpretable word and word sense representations. Given a collection of usage examples for a target word, and the co…
Language ModelingLanguage ModellingSemantic SimilaritySemantic Textual Similarity+2Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition
Natural language definitions of terms can serve as a rich source of knowledge, but structuring them into a comprehensible semantic model is essential to enable them to be used in semantic interpretation tasks. We propose…
World KnowledgeWhat Are Research Hypotheses?
Over the past decades, alongside advancements in natural language processing, significant attention has been paid to training models to automatically extract, understand, test, and generate hypotheses in open and scienti…
Natural Language Understanding