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Evaluating candidate architectures in neural architecture search (NAS) faces an inherent trade-off: on the one hand, reliable performance labels are limited because training and evaluating architectures is costly; on the other hand, zero-cost proxies (ZCPs) are cheap to compute at large scale but can be noisy. Yet, how...
This framework is the first to address catastrophic forgetting by leveraging models in CL as their own memory buffers by exploiting an implicit bias of gradient-based neural networks due to which these converge to margin maximization points.
Continual learning (CL) aims to incrementally update machine learning models from a stream of data without forgetting previously acquired knowledge. CL is highly relevant in many real-world applications such as manufacturing, where storing historical data for retraining is often infeasible due to volume, governance, or...
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