Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

With the growing popularity of deep-learning based NLP models, comes a need for
interpretable systems. But what is interpretability, and what constitutes a high-quality …

Deep learning has achieved great successes in many important areas to dealing with text,
images, video, graphs, and so on. However, the black-box nature of deep artificial neural …

Interpretation methods to reveal the internal reasoning processes behind machine learning
models have attracted increasing attention in recent years. To quantify the extent to which …

Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …

In recent years, black-box models have developed rapidly because of their high accuracy.
Balancing the interpretability and accuracy is increasingly important. The lack of …

Deep neural networks have achieved near-human accuracy levels in various types of
classification and prediction tasks including images, text, speech, and video data. However …

Recently, a significant amount of research has been investigated on interpretation of deep
neural networks (DNNs) which are normally processed as black box models. Among the …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …