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.

该项目会自动生成可以使用神经网络和其他机器学习模型作为子例程的程序。该项目的新策略通过学习项目的影响使生成此类程序变得更加容易，这是对可以学习迈向人工智能（AI）系统的系统，从而可以发展进一步的AI系统。从技术角度来看，项目通过将符号代码的离散与连续形式相关联，以一种共同生成代码和神经网络权重的方式结果将在生成三维HIC程序上展示，将几何形状的隐式神经表示与离散的图形原始词混合在一起。