17 citations · 31 across the 7 of their papers we have counts for
5 papers · 1 filter
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
Indranil Sur, Zachary Daniels, Abrar Rahman +16
As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adap…
Dynamically Throttleable Neural Networks (TNN)
Hengyue Liu, Samyak Parajuli, Jesse Hostetler +2
Conditional computation for Deep Neural Networks (DNNs) reduce overall computational load and improve model accuracy by running a subset of the network. In this work, we present a…
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer
Aswin Raghavan, Jesse Hostetler, Indranil Sur +2
We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can sample…
Toward Runtime-Throttleable Neural Networks
Jesse Hostetler
As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded contr…
Generative Memory for Lifelong Reinforcement Learning
Aswin Raghavan, Jesse Hostetler, Sek Chai
Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifet…