17 citations · 19 across the 3 of their papers we have counts for
4 papers
TACOS: Task Agnostic Continual Learning in Spiking Neural Networks
Nicholas Soures, Peter Helfer, Anurag Daram +2
Catastrophic interference, the loss of previously learned information when learning new information, remains a major challenge in machine learning. Since living organisms do not se…
Towards Continually Learning Application Performance Models
Ray A. O. Sinurat, Anurag Daram, Haryadi S. Gunawi +2
Machine learning-based performance models are increasingly being used to build critical job scheduling and application optimization decisions. Traditionally, these models assume th…
Design Principles for Lifelong Learning AI Accelerators
Dhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah +9
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The…
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…