Why & When Deep Learning Works: Looking Inside Deep Learnings
This work provides foundational insights for researchers and practitioners in machine learning, though it is incremental as it synthesizes existing research rather than introducing new methods.
The paper tackles the challenge of understanding why and when deep learning works by compiling insights from multiple researchers, resulting in five papers that explore expressiveness, limitations, and interpretability of deep networks.
The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of "Why & When Deep Learning works", with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The output of this challenge resulted in five papers that address different facets of deep learning. These different facets include a high-level understating of why and when deep networks work (and do not work), the impact of geometry on the expressiveness of deep networks, and making deep networks interpretable.