64 citations · 316 across the 28 of their papers we have counts for
5 papers · 1 filter
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…
Watch Your Step: Optimal Retrieval for Continual Learning at Scale
Truman Hickok, Dhireesha Kudithipudi
In continual learning, a model learns incrementally over time while minimizing interference between old and new tasks. One of the most widely used approaches in continual learning…
Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System
Abdullah M. Zyarah, Dhireesha Kudithipudi
Pushing the frontiers of time-series information processing in the ever-growing domain of edge devices with stringent resources has been impeded by the systems' ability to process…
Continual Learning and Catastrophic Forgetting
Gido M. van de Ven, Nicholas Soures, Dhireesha Kudithipudi
This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data. Although continual learning i…
Probabilistic Metaplasticity for Continual Learning with Memristors
Fatima Tuz Zohora, Vedant Karia, Nicholas Soures +1
Edge devices operating in dynamic environments critically need the ability to continually learn without catastrophic forgetting. The strict resource constraints in these devices po…