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新方法无需递归即可训练循环网络

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为监督记忆训练（SMT）的新方法，可以在不依赖传统递归的情况下预训练循环神经网络（RNN）。SMT通过将过程简化为记忆转换上的监督学习来训练RNN，绕过了随时间反向传播的限制。这种方法实现了时间并行训练和稳定的梯度路径，有可能实现能够捕获长期依赖关系的更具可扩展性的RNN架构。 AI

影响 实现了RNN的并行训练，有可能为构建时间抽象的模型解锁扩展能力。

排序理由 该集群包含一篇详细介绍一种新神经网络训练方法的学术论文。

在 arXiv cs.LG 阅读 →

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新方法无需递归即可训练循环网络

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该集群包含一篇详细介绍一种新神经网络训练方法的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Akarsh Kumar, Phillip Isola ·

    无需递归地预训练循环网络

    arXiv:2606.06479v1 Announce Type: new Abstract: Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT) addresses this problem poorly: it is sequential in time, limiting parallelism, a…

  2. arXiv cs.AI TIER_1 English(EN) · Phillip Isola ·

    无需递归的循环网络预训练

    Training recurrent neural networks (RNNs) requires assigning credit across long sequences of computations. Standard backpropagation through time (BPTT) addresses this problem poorly: it is sequential in time, limiting parallelism, and suffers from vanishing or exploding gradients…

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