14 citations · 22 across the 5 of their papers we have counts for
5 papers
Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
Conor F. Hayes, Felipe Leno Da Silva, Jiachen Yang +15
Deep Symbolic Optimization (DSO) is a novel computational framework that enables symbolic optimization for scientific discovery, particularly in applications involving the search f…
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…
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis
Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl +4
Sepsis is a life-threatening condition affecting one million people per year in the US in which dysregulation of the body's own immune system causes damage to its tissues, resultin…