17 citations · 33 across the 9 of their papers we have counts for
5 papers · 1 filter
Continual learning benefits from multiple sleep mechanisms: NREM, REM, and Synaptic Downscaling
Brian S. Robinson, Clare W. Lau, Alexander New +4
Learning new tasks and skills in succession without losing prior learning (i.e., catastrophic forgetting) is a computational challenge for both artificial and biological neural net…
Curvature-informed multi-task learning for graph networks
Alexander New, Michael J. Pekala, Nam Q. Le +3
Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws…
Latent Properties of Lifelong Learning Systems
Corban Rivera, Chace Ashcraft, Alexander New +2
Creating artificial intelligence (AI) systems capable of demonstrating lifelong learning is a fundamental challenge, and many approaches and metrics have been proposed to analyze a…
L2Explorer: A Lifelong Reinforcement Learning Assessment Environment
Erik C. Johnson, Eric Q. Nguyen, Blake Schreurs +6
Despite groundbreaking progress in reinforcement learning for robotics, gameplay, and other complex domains, major challenges remain in applying reinforcement learning to the evolv…
Lifelong Learning Metrics
Alexander New, Megan Baker, Eric Nguyen +1
The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously…