- author = "Joanne T. Kim and Mikel Landajuela and Brenden K. Petersen",
- title = "Distilling Wikipedia Mathematical Knowledge into Neural Network Models",
- booktitle = "1st Mathematical Reasoning in General Artificial Intelligence Workshop at ICLR 2021",
- year = "2021",
- editor = "Yuhuai (Tony) Wu and Kshitij Bansal and Wenda Li and Melanie Mitchell and David McAllester and John Harrison",
- month = may # " 7",
- keywords = "genetic algorithms, genetic programming: Poster",
-
URL = "
https://mathai-iclr.github.io/papers/papers/MATHAI_15_paper.pdf",
-
URL = "
https://mathai-iclr.github.io/papers/posters/MATHAI_15_poster.png",
-
URL = "https://arxiv.org/abs/2104.05930",
-
URL = "https://www.osti.gov/servlets/purl/1782518",
- size = "6 pages",
- abstract = "Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralised source of real-world symbolic expressions to be used as training data. In contrast, the field of natural language processing leverages resources like Wikipedia that provide enormous amounts of real-world textual data. Adopting the philosophy of mathematics as language, we bridge this gap by introducing a pipeline for distilling mathematical expressions embedded in Wikipedia into symbolic encodings to be used in downstream machine learning tasks. We demonstrate that a mathematical language model trained on this corpus of expressions can be used as a prior to improve the performance of neural-guided search for the task of symbolic regression.",
-
notes = "Is this GP?
Math-AI https://mathai-iclr.github.io/ https://mathai-iclr.github.io/papers/
Lawrence Livermore National Laboratory, Livermore, CA 94550, USA",
