Continual Learning with TAGI

In this seminar, I explored TAGI’s capability to facilitate continual learning in neural networks. One widely studied approach in the literature involves applying regularization to model updates to prevent catastrophic forgetting.

I discussed how TAGI inherently incorporates such regularization within its parameter distribution updates, and examined whether adding noise to model parameters can improve the stability-plasticity trade-off in Bayesian neural networks.

Source: CIV-ML’s Seminars