17 citations · 29 across the 11 of their papers we have counts for
6 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…
Learning with Local Gradients at the Edge
Michael Lomnitz, Zachary Daniels, David Zhang +1
To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient…
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2
Zachary Daniels, Aswin Raghavan, Jesse Hostetler +4
One approach to meet the challenges of deep lifelong reinforcement learning (LRL) is careful management of the agent's learning experiences, to learn (without forgetting) and build…
A Dynamic Data Driven Approach for Explainable Scene Understanding
Zachary A Daniels, Dimitris Metaxas
Scene-understanding is an important topic in the area of Computer Vision, and illustrates computational challenges with applications to a wide range of domains including remote sen…
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels +8
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at th…
Saccade Mechanisms for Image Classification, Object Detection and Tracking
Saurabh Farkya, Zachary Daniels, Aswin Nadamuni Raghavan +2
We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed a…