NeuNetS：一个用于神经网络设计的自动综合引擎
NeuNetS: An Automated Synthesis Engine for Neural Network Design
摘要：
将神经网络应用于各种实际应用正在改变人工智能在实践中的应用方式。通过API或使用客户数据定制训练预构建神经网络架构的能力提供的预训练神经网络模型使开发人员对AI的使用变得更加简单，并导致这些复杂AI模型的广泛采用。虽然预构建的网络模型存在于某些场景中，但为了尝试满足每个应用程序所特有的约束，人工智能团队需要考虑开发自定义神经网络架构，以满足准确性和内存占用之间的权衡，以实现其独特用例的严格约束。然而，只有一小部分数据科学团队拥有从头开始创建神经网络所需的技能和经验，需求远远超过供应。在本文中，我们介绍了NeuNetS：一个自动化的神经网络合成引擎，用于定制神经网络设计，可作为IBM的AI OpenScale产品的一部分。NeuNetS可用于文本和图像领域，可以为特定任务构建神经网络，所需时间仅为当今人工努力的一小部分，准确性与人类设计的AI模型相似。
英文摘要：
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through APIs or capability to custom train pre-built neural network architectures with customer data has made the consumption of AI by developers much simpler and resulted in broad adoption of these complex AI models. While prebuilt network models exist for certain scenarios, to try and meet the constraints that are unique to each application, AI teams need to think about developing custom neural network architectures that can meet the tradeoff between accuracy and memory footprint to achieve the tight constraints of their unique use-cases. However, only a small proportion of data science teams have the skills and experience needed to create a neural network from scratch, and the demand far exceeds the supply. In this paper, we present NeuNetS : An automated Neural Network Synthesis engine for custom neural network design that is available as part of IBM's AI OpenScale's product. NeuNetS is available for both Text and Image domains and can build neural networks for specific tasks in a fraction of the time it takes today with human effort, and with accuracy similar to that of human-designed AI models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
