Add to ORKGA popular approach to unveiling the black box of neural NLP models is to leverage saliency methods, which assign scalar importance scores to each input component. A common practice for evaluating whether an interpretability method is faithful has been to use evaluation-by-agreement – if multiple methods agree on an explanation, its credibility increases. However, recent work has found that saliency methods exhibit weak rank correlations even when applied to the same model instance and advocated for alternative diagnostic methods. In our work, we demonstrate that rank correlation is not a good fit for evaluating agreement and argue that Pearson-r is a better-suited alternative. We further show that regularization techniques that increase fai...
Saliency methods seek to explain the predictions of a model by producing an importance map across ea...
In the past decades, hundreds of saliency models have been proposed for fixation prediction, along w...
AbstractThis paper presents a survey of feature saliency measures used in artificial neural networks...
Saliency methods are frequently used to explain Deep Neural Network-based models. Adebayo et al.'s w...
Saliency methods calculate how important each input feature is to a machine learning model's predict...
A fundamental bottleneck in utilising complex machine learning systems for critical applications has...
As the applications of Natural Language Processing (NLP) in sensitive areas like Political Profiling...
As deep learning models become increasingly complex, practitioners are relying more on post hoc expl...
Due to the black-box nature of deep learning models, there is a recent development of solutions for ...
While there is increasing concern about the interpretability of neural models, the evaluation of int...
Saliency methods provide post-hoc model interpretation by attributing input features to the model ou...
Saliency methods are widely used to visually explain 'black-box' deep learning model outputs to huma...
Conventional saliency maps highlight input features to which neural network predictions are highly s...
Neural network models such as Transformer-based BERT, mBERT and RoBERTa are achieving impressive per...
Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) ...
Saliency methods seek to explain the predictions of a model by producing an importance map across ea...
In the past decades, hundreds of saliency models have been proposed for fixation prediction, along w...
AbstractThis paper presents a survey of feature saliency measures used in artificial neural networks...
Saliency methods are frequently used to explain Deep Neural Network-based models. Adebayo et al.'s w...
Saliency methods calculate how important each input feature is to a machine learning model's predict...
A fundamental bottleneck in utilising complex machine learning systems for critical applications has...
As the applications of Natural Language Processing (NLP) in sensitive areas like Political Profiling...
As deep learning models become increasingly complex, practitioners are relying more on post hoc expl...
Due to the black-box nature of deep learning models, there is a recent development of solutions for ...
While there is increasing concern about the interpretability of neural models, the evaluation of int...
Saliency methods provide post-hoc model interpretation by attributing input features to the model ou...
Saliency methods are widely used to visually explain 'black-box' deep learning model outputs to huma...
Conventional saliency maps highlight input features to which neural network predictions are highly s...
Neural network models such as Transformer-based BERT, mBERT and RoBERTa are achieving impressive per...
Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) ...
Saliency methods seek to explain the predictions of a model by producing an importance map across ea...
In the past decades, hundreds of saliency models have been proposed for fixation prediction, along w...
AbstractThis paper presents a survey of feature saliency measures used in artificial neural networks...