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标题（学术版）：均方根误差 (RMSE)与平均绝对误差 (MAE)在损失函数中的应用与比较 标题（生动版）：RMSE与MAE：两种评价预测误差的尺子，哪个更适合你？ 摘要： 在机器学习和数据分析中，损. Her parents and grandparents forced her to leave the. 因为假设残差服从正态分布，意味着残差的均值将始终为0，所以可计算均方误差MSE、均方误差根RMSE、平均绝对误差MAE。 均方误差MSE（又称L2范数损失），即误差平方和的平均值，MSE是衡.
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这是 MAE体的架构图，预训练阶段一共分为四个部分，MASK，encoder，decoder。 MASK 可以看到一张图片进来，首先把你切块切成一个一个的小块，按格子切下来。 其中要被MASK住的这一块就是涂. Her orthodox jewish family was totally averse to her having an entertainment career MAE编码器 编码器为原始ViT，且只应用未屏蔽的patch，并采用线性投影计算这些patch的patch embedding，并添加position embedding，然后通过一系列Transformer块处理结果集。 MAE解码器 .
MSE 和 MAE 的计算方法完全不同，你可以去搜一下公式看一下。 直观理解的话，MSE是先平方，所以 放大 了 大 误差，比如，在平稳的序列点上，MAE误差为2，在波峰波谷上MAE误差.
MAE编码器 MAE的编码器是一个ViT，但只应用与可见的、未屏蔽的补丁。 就像在标准的ViT中一样，MAE的编码器通过添加了位置嵌入的线性投影来嵌入补丁，然后通过一系列Transformer块来处理. 问题本身是不是有点问题？ ViT (Vision Transformers)是模型结构，而 MAE 是在 ViT 结构上自监督训练的 masked encoder。 我猜题主想问的是，为什么用的都是ImageNet 或者 JFT300 这种. 总结 L1范数、L1损失和MAE损失在对异常值的鲁棒性方面优于L2范数、L2损失和MSE损失，但后者在数学上更光滑，更容易进行优化。 选择哪种损失函数取决于具体问题的需求和数据的特性。 前言 作为一个成功拿到了UCLA MAE项目offer的过来人，可以根据自己的申请经历，跟题主分享一些关于MAE项目的相关信息。 我目前就读于UCLA的MAE项目（Master of Applied Economics），即应用经.
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