Catastrophic Interference in Connectionist Networks¶
McCloskey, M., & Cohen, N. J. (1989). Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem. Psychology of Learning and Motivation.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Reconsolidation
- In ML fine-tuning, loading a checkpoint, applying gradients, and saving updated weights reconsolidates prior learning under new evidence, and catastrophic forgetting is reconsolidation-driven loss.
This sourceIdentifies catastrophic forgetting, where training on new data overwrites prior learning — the reconsolidation pathology in neural networks.
- In ML fine-tuning, loading a checkpoint, applying gradients, and saving updated weights reconsolidates prior learning under new evidence, and catastrophic forgetting is reconsolidation-driven loss.
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