datamllab/awsome-interpretable-ML

Awsome-Interpretable-ML

A curated, but probably biased and incomplete, list of awesome Trojan Attack in AI resources.

If you want to contribute to this list, feel free to pull a request. Also you can contact Ninghao Liu from the Data Lab at Texas A&M University through email: nhliu43@tamu.edu.

Benchmark Datasets

• Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words? (ACL20)

Local Interpretation in Computer Vision

Local Interpretation in NLP

• Attention is not Explanation (NAACL19)
  
• Attention is not not Explanation (EMNLP19)
  
• Is Attention Interpretable? (ACL19)
  

Local Interpretation in Graph Analytics

• GNNExplainer: Generating Explanations for Graph Neural Networks (NeurIPS19)

Representation Interpretation

• On Interpretation of Network Embedding via Taxonomy Induction (KDD18)
• Interpretable Basis Decomposition for Visual Explanation (ECCV18)
• Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (ICML19)
• Towards a deep and unified understanding of deep neural models in nlp (ICML19)

Interpretable Modeling

Application of ML Interpretation

• Learning Credible Deep Neural Networks with Rationale Regularization (ICDM19)