SBIR Phase I: Geometric Unified Learning

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响是，所提出的几何统一学习（GUL）技术，由可重复使用的构建块组成，克服了深度学习方法的几个缺点，并通过以下方式为最终用户带来更大的价值：1）节省数据处理的时间和资源，因为GUL具有自动数据清理和压缩功能; 2）在数据输入到深度神经网络之前进行拟合，以避免过度拟合，因为GUL可以同时搜索和学习，并选择最重要的数据来使用; 3）提供透明和值得信赖的解决方案，数据决策者可以轻松理解; 4）使数据分析对机器学习领域之外的软件开发人员、科学家和工程师来说变得有趣，因为GUL输出是可以理解的; 5）产生灵活、健壮和敏捷的调试解决方案，因为GUL的组件可以分解或重新组织，以形成针对不同问题的新解决方案; 6）节省资金，因为GUL并行运行速度更快，只使用原始数据点的1%左右。第一阶段项目有几个智力上的优点，解决了深度学习中的几个技术挑战，例如垃圾输入产生垃圾输出，需要大量的训练数据，运行时间长，无法解释的输出，过度的参数调整，然后重复直到获得所需的结果。GUL，植根于微分几何，创建适当的本地（GUL）坐标系，黎曼（GUL）度量，变换和测地线，以识别数据不变式，固有模式，以工程师和设计GUL数据适配器，特征提取器和测量，可以加强，以更好地每次最终用户运行他们的数据。GUL工具具有矢量化数据，压缩数据，同时搜索和学习的能力，具有高度可解释的结果。当该技术进行预测时，它将向用户准确显示哪些数据点对这些预测负责。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.