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Enhancing network modularity to mitigate catastrophic forgetting
2020
Applied Network Science
AbstractCatastrophic forgetting occurs when learning algorithms change connections used to encode previously acquired skills to learn a new skill. Recently, a modular approach for neural networks was deemed necessary as learning problems grow in scale and complexity since it intuitively should reduce learning interference by separating functionality into physically distinct network modules. However, an algorithmic approach is difficult in practice since it involves expert design and trial and
doi:10.1007/s41109-020-00332-9
fatcat:3wqc7beworfg5lmafo6aqjowu4