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https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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diffusion bracket fix
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@ -561,7 +561,7 @@
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<url>
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<loc>https://nn.labml.ai/transformers/models.html</loc>
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<lastmod>2022-06-28T16:30:00+00:00</lastmod>
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<lastmod>2022-07-02T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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@ -652,7 +652,7 @@
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<url>
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<loc>https://nn.labml.ai/transformers/basic/autoregressive_experiment.html</loc>
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<lastmod>2022-06-28T16:30:00+00:00</lastmod>
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<lastmod>2022-07-02T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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@ -813,7 +813,7 @@
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<url>
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<loc>https://nn.labml.ai/transformers/positional_encoding.html</loc>
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<lastmod>2021-10-21T16:30:00+00:00</lastmod>
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<lastmod>2022-07-02T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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@ -855,7 +855,7 @@
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<url>
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<loc>https://nn.labml.ai/transformers/mha.html</loc>
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<lastmod>2022-06-28T16:30:00+00:00</lastmod>
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<lastmod>2022-07-02T16:30:00+00:00</lastmod>
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<priority>1.00</priority>
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</url>
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@ -47,7 +47,7 @@ for $T$ time steps.
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\begin{align}
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\textcolor{lightgreen}{p_\theta}(x_{t-1} | x_t) &= \mathcal{N}\big(x_{t-1};
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\textcolor{lightgreen}{\mu_\theta}x_t, t), \textcolor{lightgreen}{\Sigma_\theta}(x_t, t)\big) \\
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\textcolor{lightgreen}{\mu_\theta}(x_t, t), \textcolor{lightgreen}{\Sigma_\theta}(x_t, t)\big) \\
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\textcolor{lightgreen}{p_\theta}(x_{0:T}) &= \textcolor{lightgreen}{p_\theta}(x_T) \prod_{t = 1}^{T} \textcolor{lightgreen}{p_\theta}(x_{t-1} | x_t) \\
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\textcolor{lightgreen}{p_\theta}(x_0) &= \int \textcolor{lightgreen}{p_\theta}(x_{0:T}) dx_{1:T}
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\end{align}
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