49 citations · 122 across the 14 of their papers we have counts for
5 papers · 1 filter
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt +3
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believ…
Incorporating domain knowledge into neural-guided search
Brenden K. Petersen, Claudio P. Santiago, Mikel Landajuela
Many AutoML problems involve optimizing discrete objects under a black-box reward. Neural-guided search provides a flexible means of searching these combinatorial spaces using an a…
Improving exploration in policy gradient search: Application to symbolic optimization
Mikel Landajuela, Brenden K. Petersen, Soo K. Kim +5
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols. In contrast to tradi…
Distilling Wikipedia mathematical knowledge into neural network models
Joanne T. Kim, Mikel Landajuela, 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 train…
Reinforcement Learning for Adaptive Mesh Refinement
Jiachen Yang, Tarik Dzanic, Brenden Petersen +9
Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate c…