Exclusion and Inclusion -- A model agnostic approach to feature importance in DNNs
arXiv:2007.16010 · doi:10.1109/SSCI50451.2021.9659843
Abstract
Deep Neural Networks in NLP have enabled systems to learn complex non-linear relationships. One of the major bottlenecks towards being able to use DNNs for real world applications is their characterization as black boxes. To solve this problem, we introduce a model agnostic algorithm which calculates phrase-wise importance of input features. We contend that our method is generalizable to a diverse set of tasks, by carrying out experiments for both Regression and Classification. We also observe that our approach is robust to outliers, implying that it only captures the essential aspects of the input.
arXiv comment8 pages, 4 figures
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- Neural Machine Translation by Jointly Learning to Align and Translate
- Axiomatic Attribution for Deep Networks
- Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
- Understanding Neural Networks through Representation Erasure
- Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
- Hierarchical interpretations for neural network predictions