arXiv Machine Learning By Tieliang Gong, Zhongbo Zhang, Wen Wen, Yong-Jin Liu

Drift and Dependence: Layer-wise Information-Theoretic Bounds for Replay-Based Continual Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 11690v1 Announce Type: new Abstract: Continual learning must absorb new tasks without erasing old ones, and replay---mixing a small buffer of past examples into current training---is among the most effective remedies for catastrophic forgetting.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 18

Metaplasticity as adaptive gradient preconditioning for incremental learning

arXiv:2608. 14634v1 Announce Type: new Abstract: Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation process driven at the local level by synaptic metaplasticity: the continuous, history-dependent neuromodulation of individual synapses.

By Isabelle Aguilar, Zayn Andre Zainal, Omid Kavehei