arXiv Machine Learning September 25, 2026 By Krsto Prorokovi\'c

Online Task Adaptation via Self-Organisation

Read the original on arXiv Machine Learning →

The paper proposes a method for task adaptation that eliminates the need for gradient computation during adaptation. Using a Neural Cellular Automaton, the authors train recurrent dynamics and memory read/write operations via backpropagation, then fix the slow model parameters. Online adaptation is achieved solely through local memory updates driven by prediction errors, enabling significant performance gains on new classification tasks with a single support set pass.

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