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    周志华等提出RNN可解释性方法,看看RNN内部都干了些什么

    对于具体解释 RNN 中的什么,研究者分析了门控机制在 LSTM、GRU 和 MGU 等模型中的作用,特别是不同门控 RNN 中门的数量及其影响。 我们的结果表明有简单门控结构的 RNN 例如最小门控单元(MGU)的表现更好,并且 FSA 中的转换可以得到和对应单词相关的特定分类结果,该过程对于人类而言是可理解的。 为了验证该结论,我们展示了在 MGU、SRU、GRU 和 LSTM 上的隐藏状态点的分布,如图 1(a)到(d)所示。 ? 如表 2 所示,我们可以看到在从 MGU 学习到的 FSA 的平均集群数量总是能以最小的集群数量达到准确率 1.0。 图 7:在情感分析任务上训练的 MGU 学习到的 FSA。这里的 FSA 经过压缩,并且相同方向上的相同两个状态之间的词被分成同一个词类。

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    mac 上学习k8s系列(18)CRD (part I)

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    在 Kubernetes 中使用 Keycloak OIDC Provider 对用户进行身份验证

    eyJhbGciOiJSUzI1NiIsInR5cCIgOiAiSldUIiwia2lkIiA6ICJZUFB3M0l2MHQ5WHEzMW0zLUtCemgyaHk3Um1LSEJ5dEtIdWhHSWY4Vkw0In0.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