1 citations · 1 across the 3 of their papers we have counts for
3 papers
Not Just After One: Sleep-Inspired Replay Prevents Catastrophic Forgetting After Sequential Tasks
Anthony Bazhenov, Jean Erik Delanois, Giri P. Krishnan
One of the critical limitations of artificial neural networks is their lack of ability to continually learn: training on new tasks often leads to interference and forgetting of the…
Unsupervised Replay Strategies for Continual Learning with Limited Data
Anthony Bazhenov, Pahan Dewasurendra, Giri P. Krishnan +1
Artificial neural networks (ANNs) show limited performance with scarce or imbalanced training data and face challenges with continuous learning, such as forgetting previously learn…
Sleep-Like Unsupervised Replay Improves Performance when Data are Limited or Unbalanced
Anthony Bazhenov, Pahan Dewasurendra, Giri Krishnan +1
The performance of artificial neural networks (ANNs) degrades when training data are limited or imbalanced. In contrast, the human brain can learn quickly from just a few examples.…