Deep neural networks have constantly pushed the state-of-the-art performance in natural language processing and are considered as the de-facto modeling approach in solving complex NLP tasks such as machine translation, summarization and question-answ
ering. Despite the proven efficacy of deep neural networks at-large, their opaqueness is a major cause of concern. In this tutorial, we will present research work on interpreting fine-grained components of a neural network model from two perspectives, i) fine-grained interpretation, and ii) causation analysis. The former is a class of methods to analyze neurons with respect to a desired language concept or a task. The latter studies the role of neurons and input features in explaining the decisions made by the model. We will also discuss how interpretation methods and causation analysis can connect towards better interpretability of model prediction. Finally, we will walk you through various toolkits that facilitate fine-grained interpretation and causation analysis of neural models.
This research discusses the most important pieces of evidence put forward by Ash'ari thinkers on two very important issues: formation of the world and the idea of causality. Depending on IbnRushd's reading, the paper also highlights the most import
ant loose and weak points Ash'ari inferences. Ibin Rushd, a pioneer of the intellectual trend in Islamic philosophy, saw in the treatment of these two issues represented an epistemological obstacle faced by Arab-Islamic mind and stood between it and the causal understanding of the universe. The philosopher Averroes sensed the danger of the absence of objective consciousness of the universe and the nature in Arabic and Islamic philosophy. In an attempt transform society from a state of theological submission to one of scientific certainty, he tried to lay the ground for a vision depending on the mind.
