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<title>MIStatlE</title>
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<description>数学、算法与 AI 的笔记：结构、机制与边界。</description>
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  <title>Littlestone Dimension</title>
  <link>https://mistatle.github.io/notes/littlestone-dimension/</link>
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  <pubDate>Sun, 11 Oct 2026 16:00:00 GMT</pubDate>
  <description>Littlestone dimension 的定义、最优错误界与 SOA 证明，并分析它与 VC dimension 的区别。</description>
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  <title>Policy Gradient 的优化视角</title>
  <link>https://mistatle.github.io/notes/policy-gradient-optimization/</link>
  <guid>https://mistatle.github.io/notes/policy-gradient-optimization/</guid>
  <pubDate>Tue, 05 May 2026 16:00:00 GMT</pubDate>
  <description>从目标函数、梯度估计、方差与步长选择建立一条连续的优化主线。</description>
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  <title>LeWorldModel / SIGReg：为什么不会塌缩？</title>
  <link>https://mistatle.github.io/notes/leworldmodel-sigreg/</link>
  <guid>https://mistatle.github.io/notes/leworldmodel-sigreg/</guid>
  <pubDate>Tue, 24 Mar 2026 16:00:00 GMT</pubDate>
  <description>连接 latent prediction、表示塌缩与 SIGReg，辨认机制真正依赖的条件。</description>
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  <title>Typical Set：高维空间中的概率集中</title>
  <link>https://mistatle.github.io/notes/typical-set/</link>
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  <pubDate>Tue, 20 Jan 2026 16:00:00 GMT</pubDate>
  <description>概率质量为什么不在密度最高的点，而会落在高维空间中的薄壳上？</description>
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  <title>缩放点积注意力：为什么除以 √dₖ？</title>
  <link>https://mistatle.github.io/notes/scaled-dot-product-attention/</link>
  <guid>https://mistatle.github.io/notes/scaled-dot-product-attention/</guid>
  <pubDate>Sat, 22 Nov 2025 16:00:00 GMT</pubDate>
  <description>从点积方差、Softmax 饱和与梯度稳定性出发，推导 Transformer 中缩放因子的来历，并给出 PyTorch 实现。</description>
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