DOLPHIN让神经符号学习在复杂任务上更快更稳地收敛。
Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning
- 用Python编写符号程序，CPU处理复杂推理，GPU加速概率计算与梯度传播。
- 13个基准测试中，复杂任务全部收敛，最快比基线快62倍。
- 适合需要大规模符号推理的科研人员和工程师使用。
神经符号学习融合符号推理与深度学习，但难以扩展至复杂符号程序或大规模数据集。我们提出DOLPHIN框架，支持用Python编写神经符号程序，在CPU上执行复杂符号推理，同时将概率计算和梯度传播向量化并部署于GPU。在涵盖文本、图像、视频数据的13个基准测试中，涉及递归与黑箱函数等符号特性，DOLPHIN在更复杂的任务上达到当前最优准确率，而Scallop、ISED、IndeCateR+等现有框架在时间限制内无法收敛。在简单任务上，DOLPHIN性能相当，但速度提升1.71倍至62倍。整体上，DOLPHIN显著提升了神经符号框架的可扩展性，在困难任务中实现最先进的效率与收敛能力。代码已开源：https://github.com/Dolphin-NeSy/Dolphin。
原文摘要 · Abstract (English)
Neurosymbolic learning enables the integration of symbolic reasoning with deep learning but faces significant challenges in scaling to complex symbolic programs, large datasets, or both. We introduce DOLPHIN, a framework that tackles these challenges by supporting neurosymbolic programs in Python, executing complex symbolic reasoning on the CPU while vectorizing probabilistic computations and gradient propagation on the GPU. Across 13 benchmarks spanning tasks over text, image, and video data, with symbolic reasoning features like recursion and black-box functions, DOLPHIN converges to state-of-the-art accuracies on the more complex benchmarks while existing frameworks such as Scallop, ISED, and IndeCateR+ fail to converge within the time limit. On simpler benchmarks, DOLPHIN matches their performance, while achieving these results 1.71x to 62x faster than the baselines. Overall, DOLPHIN advances the scalability of neurosymbolic frameworks, achieving state-of-the-art efficiency and convergence on difficult benchmarks where existing frameworks struggle. The code is published at https://github.com/Dolphin-NeSy/Dolphin.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。