ePoster · Machine Learning
Evaluating Memory Behavior in Continual Learning using the Posterior in a Binary Bayesian Network
Akshay Bedhotiya, Emre Neftci · Bernstein Conference 2024
Continual learning involves a network adapting to new data while retaining previously learned information. In Artificial Neural Networks , this process faces the challenge of catastrophic forgetting, where updating parameters with new data causes an exponential decline in accuracy over time for previously learned tasks. Several methods have been proposed to address this problem and enable continual learning on future neuromorphic hardware. One such neuroscience-inspired method is metaplasticity, which refers to a network's ability to change the learning capacity of its synapses based on its cu