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Causality and Deep Learning: Synergies, Challenges and the Future

Nan Rosemary Ke · Stefan Bauer

Moderator : Mohammad Taha Bahadori

Hall F


Deep neural networks have achieved outstanding success in many tasks ranging from computer vision, to natural language processing, and robotics. However such models are still pale in their ability to understand the world around us, as well as generalizing and adapting to new tasks or environments. One possible solution to this problem are models that comprehend causality, since such models can reason about the connections between causal variables and the effect of intervening on them. However, existing causal algorithms are typically not scalable nor applicable to highly nonlinear settings, and they also assume that the causal variables are meaningful and given. Recently, there has been an increased interest and research activity at the intersection of causality and deep learning in order to tackle the above challenges, which use deep learning for the benefit of causal algorithms and vice versa. This tutorial is aimed at introducing the fundamental concepts of causality and deep learning for both audiences, providing an overview of recent works, as well as present synergies, challenges and opportunities for research in both fields.

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