在线任务自适应：基于自组织机制
Online Task Adaptation via Self-Organisation
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中文总结 AI 辅助
本文提出一种基于神经细胞自动机的元学习自组织机制，通过快速记忆更新实现无梯度的在线任务自适应，在保留分类任务上显著提升性能。
中文摘要 AI 辅助
神经网络通常通过计算梯度和更新模型参数来进行自适应。我们研究任务特定的自适应是否可以从一个元学习的自组织过程中涌现，该过程在自适应时不需要梯度。我们通过神经细胞自动机实例化这一思想，其中局部交互的循环细胞同时维持循环状态和快速联想记忆。在元训练期间，使用反向传播学习循环动力学以及记忆的读写方式。训练完成后，慢速模型参数保持固定，在线自适应仅通过由局部预测误差和delta规则驱动的逐细胞记忆更新发生。我们评估所学机制能否适应语义不同的保留分类任务。对支持数据的单次遍历即可在保留性能上产生显著提升，而无需在自适应期间进行梯度计算或参数更新，并且该机制在联合处理的示例数量发生大变化时仍然有效。这些结果表明，任务特定的自适应可以通过显式快速记忆更新实现，同时保持慢速模型参数固定。
英文摘要
Neural networks are typically adapted by computing gradients and updating model parameters. We investigate whether task-specific adaptation can instead emerge from a meta-learned self-organising process that requires no gradients at adaptation time. We instantiate this idea with a Neural Cellular Automaton in which locally interacting recurrent cells maintain both a recurrent state and a fast associative memory. During meta-training, backpropagation is used to learn the recurrent dynamics together with how the memory is read and written. Once training is complete, the slow model parameters remain fixed, and online adaptation occurs only through cellwise memory updates driven by local prediction errors and a delta rule. We evaluate whether the learned mechanism can adapt to semantically distinct held-out classification tasks. A single pass over the support data produces substantial improvements in held-out performance without gradient computation or parameter updates during adaptation, and the mechanism remains effective across large changes in the number of examples processed jointly. These results show that task-specific adaptation can be achieved through explicit fast-memory updates while keeping the slow model parameters fixed.
