Grzegorz Chrupała avatar Grzegorz Chrupała committed 79f58a9

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doc/clin-2012/colada-best-mrr-10K.pdf

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doc/clin-2012/delta-h-alpha-mrr.pdf

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doc/clin-2012/plots.R

 #pdf('delta-h-alpha-K.pdf', height=3.7,width=5)
 par(mar=c(5.1, 4.1, 1.1, 2.1))
 plot(alpha, K/1000, xlab=expression(alpha), ylim=c(0.7,5),
-    ylab="K", type='b', lwd=2, col="blue", log="y")
+    ylab="K/1000", type='b', lwd=2, col="blue", log="y")
 par(new=T)
 plot(alpha, K.active/K, ylab="", xlab="", axes=F, type='b', col="red")
 axis(4, pretty(c(0.01, 1)))
        fill=c("blue","red"),cex=1.2,bg="white")
 dev.off()
 
-pdf('colada-best-mrr-10K.pdf', height=3.7, width=5)
+pdf('colada-best-mrr-10K.pdf', height=4, width=5)
 dat <- read.table('colada-best-mrr-all.csv', header=T)
+batch <- read.table('lda-batch-mrr.csv', header=T)
 K <- c(50, 200, 800)
 plot(K, dat$mrr[dat$pass==1], ylim=c(0.3, 0.45), 
     xlab="K", ylab="MRR",
     type='b', log="x", lwd=2, col="red")
-points(K, dat$mrr[dat$pass==20], type='b', lwd=2, col="blue") 
-legend("topleft",legend=c(expression(j==20), expression(j==1)),
-       fill=c("blue","red"),cex=1.2,bg="white")
+points(K, dat$mrr[dat$pass==20], type='b', lwd=2, col="blue")
+points(K, batch$mrr, type='b', lwd=1, col="gray")
+legend("topleft",legend=c("batch", "20 passes", "1 pass"),
+       fill=c("gray", "blue","red"),cex=1.2,bg="white")
 dev.off()
+
+pdf('side-by-side.pdf', height=5, width=8)
+layout(matrix(1:2, ncol=2))
+plot(alpha, mrr, xlab=expression(alpha), ylab="MRR", ylim=c(0.1,0.5),
+     cex.lab=1.2, cex.axis=1.2, type='b', lwd=2)
+abline(0.4, 0, lwd=1, col="gray")
+plot(K, dat$mrr[dat$pass==20], xlab="K", ylab="", ylim=c(0.1,0.5) , type='b', lwd=2, col="blue", log="x")
+points(K, dat$mrr[dat$pass==1], type='b', lwd=2, col="red")
+abline(0.4, 0, lwd=1, col="gray")
+dev.off()
+
+
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doc/clin-2012/slides.pdf

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doc/clin-2012/slides.tex

  \end{frame}
 
  \begin{frame}
-   \frametitle{Word classes with online LDA (coLaDA)}
+   \frametitle{Word classes with online LDA (CoLaDA)}
    \begin{itemize}
    \item $d$ - word type
    \item $w$ - context feature
  \begin{frame}
    \frametitle{CoLaDA results}
    \begin{center}
+\vskip -0.5cm
      \includegraphics[scale=0.8]{colada-best-mrr-10K.pdf}
    \end{center}
  \end{frame}
    \item Multiple passes help a bit
    \item Best parameters
      \begin{itemize}
-     \item 1 pass: $\sum_1^K \alpha = 0.1$, $\beta = 0.01$
-     \item 20 passes: $\sum_1^K \alpha = 10$, $\beta = 0.1$
+     \item 1 pass: $K \alpha = 0.1$, $\beta = 0.01$
+     \item 20 passes: $K \alpha = 10$, $\beta = 0.1$
      \end{itemize}
    \item Clusters don't always ``look'' as coherent as with batch LDA
    \end{itemize}
  \end{frame}
 
+ \begin{frame}\frametitle{$\Delta$H vs CoLaDA}
+
+   \begin{center}
+\vskip -1cm
+     \includegraphics[scale=0.5]{side-by-side.pdf}
+   \end{center}
+
+ \end{frame}
+
+ \begin{frame}
+   \frametitle{Conclusion} Look at models from complementary
+   perspectives:
+\vskip 0.5cm
+   \begin{itemize}
+\item Make the cognitive model more flexible
+  \begin{itemize}
+  \item Learn more about it
+  \item  Make it tweakable
+  \end{itemize}
+\item Impose cognitive plausibility on practical model
+  \begin{itemize}
+  \item Improve memory efficiency
+  \item Learn from data streams
+  \end{itemize}
+\end{itemize}
+\end{frame}
+
  \begin{frame}
    \frametitle{Future}
    \begin{itemize}
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