no code implementations • 19 Jul 2021 • Brenden K. Petersen, Claudio P. Santiago, Mikel Landajuela Larma
Many AutoML problems involve optimizing discrete objects under a black-box reward.
1 code implementation • 19 Jul 2021 • Mikel Landajuela Larma, Brenden K. Petersen, Soo K. Kim, Claudio P. Santiago, Ruben Glatt, T. Nathan Mundhenk, Jacob F. Pettit, Daniel M. Faissol
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols.
no code implementations • 13 Apr 2021 • Joanne T. Kim, Mikel Landajuela Larma, Brenden K. Petersen
Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralized source of real-world symbolic expressions to be used as training data.
1 code implementation • ICLR 2021 • Brenden K. Petersen, Mikel Landajuela Larma, T. Nathan Mundhenk, Claudio P. Santiago, Soo K. Kim, Joanne T. Kim
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.