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doc/pub/week16/pdf/week16.pdf

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doc/src/week16/Latexfiles/week16.tex

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@@ -368,7 +368,7 @@ \section{Mathematical Formulation}
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\item Fake images are \textbf{detached} from the computation graph during
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the $D$ step to prevent spurious gradients flowing into $G$.
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\item Both networks update simultaneously — this does \emph{not} guarantee
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convergence (see Section~\ref{sec:challenges}).
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convergence).
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\end{itemize}
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\end{frame}
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@@ -1399,7 +1399,6 @@ \section{Summary of course}
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\item Boltzmann machines
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\item VAEs
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\item Diffusion models
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\item Autoregressive
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\item GANs
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\end{itemize}
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\end{columns}
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\end{block}
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\vspace{3pt}
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\begin{alertblock}{Goal}
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Build a NN from first principles
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Build an NN from first principles
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before using high-level frameworks.
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\end{alertblock}
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\end{columns}
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\item Explicit vs.\ implicit density estimation
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\item Energy-based models:
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$p_\theta(\bm{x}) = e^{-E_\theta(\bm{x})}/Z(\theta)$
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\item Markov Chain Monte Carlo recap
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\item Markov Chain Monte Carlo
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\item Gibbs sampling
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\end{itemize}
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\end{block}
@@ -1982,7 +1981,7 @@ \section{Summary of course}
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Schr\"{o}dinger equation
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\item \textbf{Reaction pathways:}
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transformers for retrosynthesis and
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transition-state prediction
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transition-state prediction, catalysis
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\end{itemize}
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\end{block}
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\end{columns}
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ice-sheet dynamics, and
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ocean surface reconstruction
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\item \textbf{Subsurface imaging:}
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generative models for reservoir
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characterisation and seismic
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reservoir characterisation and seismic
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interpretation
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\end{itemize}
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\end{block}

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