SHF: Small: Synthesizing Mixed Discrete/Continuous Programs with the Neurosymbolic Librarian

SHF：小型：与神经符号图书馆员综合混合离散/连续程序

• 批准号: 2310350
• 负责人: Kevin Ellis
• 金额: $ 60万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310350&HistoricalAwards=false
• 关键词: SHF; Small; Synthesizing; Mixed; Discrete

This project concerns automatically generating computer programs that can use neural networks and other machine learning models as subroutines. Programs like these are important because they form the cornerstone of modern machine learning systems, and also because symbolic programs and neural networks are complementary in their abilities, so such systems could learn to solve many diverse problems using a mixture of neural networks and symbolic code. However such programs are difficult to automatically generate or synthesize. The project’s novelties are new strategies that makes it much easier to generate such programs by using machine learning. The project's impact is a step toward systems that could learn to solve new problems using a mixture of neural networks and symbolic code, as well as a step toward Artificial Intelligence (AI) systems that could assist the development of further AI systems.From a technical perspective the project presents a way of jointly generating symbolic code and neural network weights both using gradient descent, by relaxing the discrete space of symbolic code into a continuous form. Because convergence of this relaxation can be difficult, the investigator proposes learning to generate the code in a multitask setting, which allows learning across many problems to aid convergence. The results will be showcased on generating 3-dimensional graphics programs, mixing implicit neural representations of geometry with discrete graphics primitives, as well as a few-shot learning domain.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.

该项目涉及自动生成可以使用神经元网络和其他机器学习模型作为子例程的计算机程序。这样的程序很重要，因为它们构成了现代机器学习系统的基石，也是因为其能力完整的符号程序和神经元网络是完整的，因此这样的系统可以学会使用神经元网络和符号代码的混合来解决许多潜水员问题。但是，这样的程序很难自动生成或合成。该项目的新颖性是新策略，可以通过使用机器学习更容易地生成此类程序。该项目的影响是朝着可以学习使用神经网络和符号代码来解决新问题的一步，以及迈向人工智能（AI）系统的一步，可以帮助进一步的AI系统的发展。从技术角度来看，该项目可以通过逐渐降低空间来呈现象征性的代码和神经网络的共同生成的范围，从而呈现出一种既定的代码，从而使逐渐变形逐渐变形，从而逐渐变形。由于这种放松的融合可能很困难，因此研究者的建议将学习在多任务设置中生成代码，这使得在许多问题上学习以帮助融合。结果将显示在生成三维图形程序，将几何形状的隐式中性表示与离散的图形原始图中混合在一起，以及一些射击的学习领域。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和广泛影响的评估来诚实的支持。