Scalable Inference in Emerging Structured Domains

点击翻译按钮获取中文摘要

The amount of data in the world is growing at an exponential rate, and both science and industry increasingly need scalable tools and techniques to analyze and understand it. This need is rapidly promoting the role of machine learning in high-tech industries and data-driven disciplines. In particular, recent advances in machine learning have brought us deep learning, a game-changing approach to inference and learning that can efficiently process a large amount of data and build powerful and expressive models. However, we cannot use deep learning within the most demanding and exciting big-data domains. Whether we are considering the neural connectivity of the brain, the large-scale structure of the Universe, or relational structure of databases, today's deep learning does not have the tools and techniques to address the very high dimensional and structured instances within these domains.******My proposed research addresses this critical problem by designing methodologies for A) active subsampling of the data for handling very large instances and; B) encoding domain structure through a group of transformations that do not alter its content: its symmetries. I focus on cosmological and relational data as two high-impact domains to showcase the proposed approach in designing deep models. For the settings in which the domain structure is unknown a priori, I outline a plan to investigate the discovery of domain structure via its symmetries. ******The major deliverables of this proposal are simple, generic and effective methods and models that extend the newfound power of deep learning to handle key structures. If successful, this program will revolutionize the current approach to cosmology, which is currently built on oversimplifying assumptions. Our objective in enabling deep learning on relational data will have a significant impact on the big-data industry, by providing the technology for scalable and accurate extrapolation of the data in widely used relational databases. The novel ability of discovering domain invariances using deep models, will bring interpretability to unstructured data and will produce a condensed form of knowledge from a large number of observations. I believe this program provides an opportunity for education of truly interdisciplinary researchers that would not only help maintain the leading position of Canada in AI but also leverage this position in a timely fashion to advance other data-driven areas within science and engineering.