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Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis
A variety of methods have been proposed to try to explain how deep neural networks make their decisions.
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding.
Textual Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis
In this paper, we propose a new strategy, called Text Deconvolution Saliency (TDS), to visualize linguistic information detected by a CNN for text classification.
Adversarial Active Learning for Deep Networks: a Margin Based Approach
We propose a new active learning strategy designed for deep neural networks.
QBDC: Query by dropout committee for training deep supervised architecture
While the current trend is to increase the depth of neural networks to increase their performance, the size of their training database has to grow accordingly.
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