可并行化的神经图灵机
Parallelizable Neural Turing Machines
AI总结：
本文提出P-NTM，通过简化和并行化改进神经图灵机，实现高效执行和训练，达到与原始模型相当的性能，同时提升训练效率。
摘要：
我们介绍了一种可并行化的神经图灵机（NTM）简化版本，称为P-NTM，它重新设计了原始架构的核心操作，以实现高效的基于扫描的并行执行。我们评估了所提出的架构在一个合成基准上，该基准涉及状态跟踪、记忆和基本算术的算法问题，通过自回归解码解决。我们将其与重新审视的稳定NTM标准实现以及传统循环和注意力架构进行比较。结果表明，尽管进行了简化，所提出的模型在长度泛化性能上与原始模型相当，能够学习解决所有问题，包括未见过的序列长度，准确率完美。它还提高了训练效率，P-NTM的并行执行比标准NTM快一个数量级。最终，这项工作为开发能够表达广泛算法类别的高效神经架构做出了贡献。
英文摘要：
We introduce a parallelizable simplification of Neural Turing Machine (NTM), referred to as P-NTM, which redesigns the core operations of the original architecture to enable efficient scan-based parallel execution. We evaluate the proposed architecture on a synthetic benchmark of algorithmic problems involving state tracking, memorization, and basic arithmetic, solved via autoregressive decoding. We compare it against a revisited stable implementation of the standard NTM, as well as conventional recurrent and attention-based architectures. Results show that, despite its simplifications, the proposed model attains length generalization performance comparable to the original, learning to solve all problems, including unseen sequence lengths, with perfect accuracy. It also improves training efficiency, with parallel execution of P-NTM being up to an order of magnitude faster than the standard NTM. Ultimately, this work contributes toward the development of efficient neural architectures capable of expressing a broad class of algorithms.
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