SBIR Phase I: Geometric Unified Learning

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is that the proposed Geometric Unified Learning (GUL) technology, consisting of reusable building blocks, overcomes several disadvantages of deep learning methods and brings increased value to end-users by: 1) saving time and resources in data processing, since GUL has an auto data cleaning and compressing capability; 2) fitting data before it is input to a deep neural network to avoid overfitting since GUL can search and learn simultaneously and select the most important data to use; 3) providing transparency and trustworthy solutions that can easily be understood by data-to-decision makers; 4) making data analysis interesting for software developers, scientists and engineers outside of the machine learning field since GUL outputs are comprehensible; 5) producing flexible, robust and agile solutions for debugging since GUL’s components can be decomposed or re-organized to form new solutions for different problems; and 6) saving money since GUL runs faster in parallel and only uses around 1% of the original data points.

This Small Business Innovation Research (SBIR) Phase I project has several intellectual merits that address several technical challenges in deep learning, such as garbage-in producing garbage-out, needing large volumes of training data, long running times, unexplainable outputs, excessive parameter tuning, then repeating until the desired results are obtained. GUL, rooted in differential geometry, creates appropriate local (GUL) coordinate systems, Riemannian (GUL) metrics, transformations, and geodesics to identify data invariants, intrinsic patterns to engineer and design GUL data adaptors, characteristic extractors, and measurements that can be reinforced to be better each time an end-user runs their data.

The GUL tools have capabilities of vectorizing data, compressing data, searching and learning simultaneously, with highly interpretable results. When the technology makes predictions it will show the user exactly which data points are responsible for those predictions.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.