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Mohamed Gunady

2 accepted papers

2020

Benchmarking Deep Learning Interpretability in Time Series Predictions

NeurIPS 2020poster

Saliency methods are used extensively to highlight the importance of input features in model predictions. These methods are mostly used in vision and language tasks, and their applications to time series data is relatively unexplored. In this paper, we set out to extensively compare the performance…

2019

Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks

NeurIPS 2019poster

Recent efforts to improve the interpretability of deep neural networks use saliency to characterize the importance of input features to predictions made by models. Work on interpretability using saliency-based methods on Recurrent Neural Networks (RNNs) has mostly targeted language tasks, and their…