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Contrastive Explanations for Model Interpretability
Contrastive explanations clarify why an event occurred in contrast to an...
03/02/2021 ∙ by Alon Jacovi, et al. ∙
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Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Trust is a central component of the interaction between people and AI, i...
10/15/2020 ∙ by Alon Jacovi, et al. ∙
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Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data
The process of collecting and annotating training data may introduce dis...
10/07/2020 ∙ by Shachar Rosenman, et al. ∙
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Aligning Faithful Interpretations with their Social Attribution
We find that the requirement of model interpretations to be faithful is ...
06/01/2020 ∙ by Alon Jacovi, et al. ∙
6
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When Bert Forgets How To POS: Amnesic Probing of Linguistic Properties and MLM Predictions
A growing body of work makes use of probing in order to investigate the ...
06/01/2020 ∙ by Yanai Elazar, et al. ∙
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Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
With the growing popularity of deep-learning based NLP models, comes a n...
04/07/2020 ∙ by Alon Jacovi, et al. ∙
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Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning
We consider the situation in which a user has collected a small set of d...
10/29/2019 ∙ by Alon Jacovi, et al. ∙
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Neural network gradient-based learning of black-box function interfaces
Deep neural networks work well at approximating complicated functions wh...
01/13/2019 ∙ by Alon Jacovi, et al. ∙
14
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Understanding Convolutional Neural Networks for Text Classification
We present an analysis into the inner workings of Convolutional Neural N...
09/21/2018 ∙ by Alon Jacovi, et al. ∙
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Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
Existing applications include a huge amount of knowledge that is out of ...
04/24/2018 ∙ by Guy Hadash, et al. ∙
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