17 citations · 29 across the 11 of their papers we have counts for
7 papers · 1 filter
Compact Recurrent Transformer with Persistent Memory
Edison Mucllari, Zachary Daniels, David Zhang +1
The Transformer architecture has shown significant success in many language processing and visual tasks. However, the method faces challenges in efficiently scaling to long sequenc…
Noise-Tolerant Coreset-Based Class Incremental Continual Learning
Edison Mucllari, Aswin Raghavan, Zachary Alan Daniels
Many applications of computer vision require the ability to adapt to novel data distributions after deployment. Adaptation requires algorithms capable of continual learning (CL). C…
Efficient Model Adaptation for Continual Learning at the Edge
Zachary A. Daniels, Jun Hu, Michael Lomnitz +5
Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. This is often a false assumption. When ML models are deployed o…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
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…