Numerical Optimization and Machine Learning

华为是电信基础设施和智能设备领域的全球领导者。该公司大量参与人工智能，面临着与机器学习技术优化相关的几个问题。该项目是华为加拿大公司与蒙特卡罗理工大学四位在数值优化方面具有丰富专业知识的教授合作完成的。通过将这些专业知识引入机器学习工业应用，其目的是提高数值优化方面的知识，开发和传播专门用于机器学习的优化工具，并培养了解这些问题的高素质人才。在三年的时间里，该项目资助了6名博士生、2名博士后、14名本科生实习生和2名研究助理。监督遵循以研究人员为中心的等级结构，这在过去已被证明是成功的。算法及其相关理论是在GERAD研究中心开发的。它们是由工业合作伙伴推动的，但被设计为可以使其他应用程序受益的通用工具。代码和软件包由学生开发，并由两名专业研究人员进行验证。一旦足够成熟，他们就会与华为的工程师合作在应用程序上进行测试。这项研究的预期结果是改进现有的优化方法，更好地适应机器学习工业应用的新算法，以及更好的神经网络优化技术。这些新方法在科学出版物中有描述，软件包也以通用版本公开发布。这个项目有助于montrsamal在优化和机器学习领域的国际声誉，这对加拿大具有高度的战略意义。通过与一家在电信和人工智能领域处于领先地位的国际公司合作，研究人员获得了技术优势，使他们能够开发更先进的方法，并培养出加拿大就业市场非常需要的人才。最后，这个项目的各个方面都遵循公平、多元和包容的原则。

Huawei is a worldwide leader in telecommunication infrastructures and smart devices. The company is massively involved in AI and faces several issues related to the optimization in machine learning techniques. This project is a collaboration between Huawei Canada and four professors of Polytechnique Montréal with a diversified expertise in numerical optimization. By bringing this expertise into machine learning industrial applications, its aim is to advance knowledge on numerical optimization, to develop and disseminate optimization tools specialized for machine learning, and to train highly-qualified personnel aware of these issues. During three years, this project supports six PhD students, two postdoctoral fellows, fourteen undergraduate interns, and two research associates. Supervisions follow a hierarchical structure that revolves around the research associates and that has been proved successful in the past. Algorithms and their associated theory are developed at the GERAD research center. They are motivated by the industrial partner but are designed as generic tools that can benefit other applications. Codes and software packages are developed by students and validated by the two professional research associates. Once mature enough, they are tested on the applications in collaboration with Huawei's engineers. The anticipated outcomes of this research are the improvement of existing optimization methods, new algorithms that are better adapted to machine learning industrial applications, and better neural networks optimization techniques. The new methods are described in scientific publications and the software packages are made publicly available in their generic versions. This project contributes to the international reputation of Montréal in both fields of optimization and machine learning, which are highly strategic for Canada. By teaming up with an international company that is one of the leaders in telecommunications and in AI, researchers gain a technological edge that allows them to develop more advanced methods and train personnel that are highly desirable for the Canadian job market. Finally, all aspects of this project follow the principles of equity, diversity and inclusion.