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合并分支 'shanweiqiao' 到 'caorunzhe' 

13章文字和图片

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Chapter13/Figures/figure-a-predefined-course-planning.tex
\begin{tikzpicture}
\tikzstyle{node1}=[inner sep=0mm,minimum height=1em,minimum width=3em,fill=ugreen!10!blue!10]
\tikzstyle{node2}=[inner sep=0mm,minimum height=1em,minimum width=3em,fill=ugreen!20!blue!20]
\tikzstyle{node3}=[inner sep=0mm,minimum height=1em,minimum width=3em,fill=ugreen!30!blue!30]
\tikzstyle{node4}=[inner sep=0mm,minimum height=1em,minimum width=3em,fill=ugreen!40!blue!40]
\tikzstyle{node5}=[inner sep=0mm,minimum height=1em,minimum width=3em,fill=ugreen!50!blue!50]
\tikzstyle{node1}=[inner sep=0mm,minimum height=1.5em,minimum width=3em,fill=ugreen!10!blue!10]
\tikzstyle{node2}=[inner sep=0mm,minimum height=1.5em,minimum width=3em,fill=ugreen!20!blue!20]
\tikzstyle{node3}=[inner sep=0mm,minimum height=1.5em,minimum width=3em,fill=ugreen!30!blue!30]
\tikzstyle{node4}=[inner sep=0mm,minimum height=1.5em,minimum width=3em,fill=ugreen!40!blue!40]
\tikzstyle{node5}=[inner sep=0mm,minimum height=1.5em,minimum width=3em,fill=ugreen!50!blue!50]
\begin{scope}
\node[anchor=north west] (l1) at (0,0) {易};
\node[anchor=west] (l2) at ([xshift=10.3em,yshift=0em]l1.east) {难};
\draw [->,thick] ([xshift=-2em,yshift=-1em]l1.south) -- ([xshift=2em,yshift=-1em]l2.south);
\node[anchor=north,node1] (c1) at ([xshift=0em,yshift=-2em]l1.south) {};
\node[anchor=west,node2] (c2) at ([xshift=0em,yshift=0em]c1.east) {};
\node[anchor=west,node3] (c3) at ([xshift=0em,yshift=0em]c2.east) {};
\node[anchor=west,node4] (c4) at ([xshift=0em,yshift=0em]c3.east) {};
\node[anchor=west,node5] (c5) at ([xshift=0em,yshift=0em]c4.east) {};
\node[anchor=north] (lb1) at ([xshift=0em,yshift=-1.5em]c3.south) {(a)样本难易程度图示};
\end{scope}
\begin{scope}[yshift = -1.7in]
\foreach \i / \j / \z in
{0/0/node1, 1/0/node1, 2/0/node1, 3/0/node1, 4/0/node1, 5/0/node1, 6/0/node1,
1/1/node2, 2/1/node2, 3/1/node2, 4/1/node2, 5/1/node2, 6/1/node2,
2/2/node3, 3/2/node3, 4/2/node3, 5/2/node3, 6/2/node3,
3/3/node4, 4/3/node4, 5/3/node4, 6/3/node4,
4/4/node5, 5/4/node5, 6/4/node5,
{0/0/node1,
1/0/node1, 1/1/node2,
2/0/node1, 2/1/node2, 2/2/node3,
3/0/node1, 3/1/node2, 3/2/node3, 3/3/node4,
4/0/node1, 4/1/node2, 4/2/node3, 4/3/node4, 4/4/node5,
5/0/node1, 5/1/node2, 5/2/node3, 5/3/node4, 5/4/node5,
}
\node[anchor=north west,\z,draw=white](n\i\j) at (1.0*3em*\j + 0*0em,-1.0*1em*\i + 0*0em){};
\node[anchor=south west,\z,draw=white](n\i\j) at (1.0*3.2em*\i + 0*0em,1.5*1em*\j + 0*0em){};
\node[anchor=south west,node1,draw=white](nc) at (0,0){};
\node[anchor=north west,node1,draw=white](nc) at (0,0){};
\draw [->,very thick] ([xshift=-1em,yshift=-1em]n00.south west) -- ([xshift=19.7em,yshift=-1em]n00.south west);
\draw [->,very thick] ([xshift=-1em,yshift=-1em]n00.south west) -- ([xshift=-1em,yshift=9em]n00.south west);
\draw [->,very thick] ([xshift=-1em,yshift=1em]n00.north west) -- ([xshift=16em,yshift=1em]n00.north west);
\draw [->,very thick] ([xshift=-1em,yshift=1em]n00.north west) -- ([xshift=-1em,yshift=-9em]n00.north west);
\node[anchor=east] (x1) at ([xshift=-1em,yshift=8em]n00.south west) {数据块};
\node[anchor=north west] (y1) at ([xshift=14.5em,yshift=-1.3em]n00.south west) {训练时长};
\node[anchor=west,font=\small] (t1) at ([xshift=0.5em,yshift=0em]n52.east) {$\cdots$};
\node[anchor=west] (x1) at ([xshift=12em,yshift=2em]n00.north west) {数据块};
\node[anchor=east] (y1) at ([xshift=-1em,yshift=-7.5em]n00.north west) {轮次};
\node[anchor=west,font=\small] (t1) at ([xshift=0em,yshift=-1em]n60.south west) {$\ldots$直到模型收敛};
\node[anchor=west,node1,minimum width=0.5em] (c1) at ([xshift=3em,yshift=-2.5em]t1.east) {};
\node[anchor=south,node2,minimum width=0.5em] (c2) at ([xshift=0em,yshift=0em]c1.north) {};
\node[anchor=south,node3,minimum width=0.5em] (c3) at ([xshift=0em,yshift=0em]c2.north) {};
\node[anchor=south,node4,minimum width=0.5em] (c4) at ([xshift=0em,yshift=0em]c3.north) {};
\node[anchor=south,node5,minimum width=0.5em] (c5) at ([xshift=0em,yshift=0em]c4.north) {};
\node[anchor=north] (lb2) at ([xshift=0em,yshift=-3em]n62.south) {(b)不同训练阶段使用的数据};
\node[anchor=north] (l1) at ([xshift=0em,yshift=-0.3em]c1.south) {易};
\node[anchor=south] (l2) at ([xshift=0em,yshift=0.3em]c5.north) {难};
\end{scope}
......
Chapter13/Figures/figure-computation-of-dropout.tex
......@@ -4,7 +4,7 @@
\def\nodespace{1}
\def\picturespace{0.8}
\tikzstyle{neuronnode} = [minimum size=1.8em,circle,draw,very thick,ublue,inner sep=0pt, fill=white,align=center]
\tikzstyle{neuronnode} = [minimum size=1.8em,circle,draw=ublue,very thick,inner sep=0pt, fill=white,align=center]
%standard
\node [neuronnode] (neuron_b) at (0,0) {\scriptsize{$b^{l}$}};
......@@ -14,9 +14,9 @@
\node [neuronnode] (neuron_z) at (1.2 * \nodespace,-1.5 * \neuronsep) {\scriptsize{$z_{i}^{l+1}$}};
\node [neuronnode] (neuron_y') at (2.4 * \nodespace,-1.5 * \neuronsep) {\scriptsize{$x_{i}^{l+1}$}};
\node [anchor=north,ublue] (standard) at ([yshift=-4em]neuron_z.south) {\scriptsize{标准网络}};
\node [ublue] (standard) at ([xshift=-1em]neuron_z.west) {\scriptsize{$\mathbf{w}_{i}^{l}$}};
\node [ublue] (standard) at ([xshift=0.6em,yshift=0.3em]neuron_z.east) {\scriptsize{$f$}};
\node [anchor=north] (standard) at ([yshift=-4em]neuron_z.south) {\scriptsize{标准网络}};
\node [] (standard) at ([xshift=-1em]neuron_z.west) {\scriptsize{$\mathbf{w}_{i}^{l}$}};
\node [] (standard) at ([xshift=0.6em,yshift=0.3em]neuron_z.east) {\scriptsize{$f$}};
\draw [->,line width=0.3mm] (neuron_b.east) -- (neuron_z.west);
\draw [->,line width=0.3mm] (neuron_y3.east) -- (neuron_z.west);
......@@ -40,9 +40,9 @@
\node [neuronnode] (drop_neuron_r2) at (4.4*\nodespace,-1.5*\neuronsep) {\scriptsize{$r_{2}^{l}$}};
\node [neuronnode] (drop_neuron_r1) at (4.4*\nodespace,-2.5*\neuronsep) {\scriptsize{$r_{1}^{l}$}};
\node [anchor=north,ublue] (standard) at ([xshift=2em,yshift=-4em]drop_neuron_z.south) {\scriptsize{应用Dropout后的网络}};
\node [ublue] (standard) at ([xshift=-1em]drop_neuron_z.west) {\scriptsize{$\mathbf{w}_{i}^{l}$}};
\node [ublue] (standard) at ([xshift=0.6em,yshift=0.3em]drop_neuron_z.east) {\scriptsize{$f$}};
\node [anchor=north] (standard) at ([xshift=2em,yshift=-4em]drop_neuron_z.south) {\scriptsize{应用Dropout后的网络}};
\node [] (standard) at ([xshift=-1em]drop_neuron_z.west) {\scriptsize{$\mathbf{w}_{i}^{l}$}};
\node [] (standard) at ([xshift=0.6em,yshift=0.3em]drop_neuron_z.east) {\scriptsize{$f$}};
%structure
\draw [->,line width=0.3mm] (drop_neuron_b.east) -- (drop_neuron_z.west);
\draw [->,line width=0.3mm] (drop_neuron_y3'.east) -- (drop_neuron_z.west);
......
Chapter13/Figures/figure-difference-between-word-level-and-sequence-level-in-knowledge-distillation.tex
......@@ -54,7 +54,7 @@
\draw [decorate,decoration={brace}] ([yshift=0.1cm]prob1.north west) to node [midway,above,font=\small] {学习目标(Teacher输出)} ([yshift=0.1cm]prob4.north east);
% Vocab
\node [word,anchor=center] () at ([xshift=-0.9cm]prob11.center) {EOS};
\node [word,anchor=center] () at ([xshift=-0.9cm]prob11.center) {$\langle$eos$\rangle$};
\node [word,anchor=center] () at ([xshift=-0.9cm]prob21.center) {I};
\node [word,anchor=center] () at ([xshift=-0.9cm]prob31.center) {am};
\node [word,anchor=center] () at ([xshift=-0.9cm]prob41.center) {fine};
......@@ -72,7 +72,7 @@
\draw [-latex,thick] (prob\i.south) to ([yshift=-0.5cm]prob\i.south);
% Input
\node [word,anchor=south] (input1) at ([yshift=-1.8cm]prob1.south) {EOS};
\node [word,anchor=south] (input1) at ([yshift=-1.8cm]prob1.south) {$\langle$eos$\rangle$};
\node [word,anchor=south] (input2) at ([yshift=-1.8cm]prob2.south) {I};
\node [word,anchor=south] (input3) at ([yshift=-1.8cm]prob3.south) {am};
\node [word,anchor=south] (input4) at ([yshift=-1.8cm]prob4.south) {fine};
......@@ -137,7 +137,7 @@
\draw [decorate,decoration={brace}] ([yshift=0.1cm]prob1.north west) to node [midway,above,font=\small] {学习目标(Teacher输出)} ([yshift=0.1cm]prob4.north east);
% Vocab
\node [word,anchor=center] () at ([xshift=-0.9cm]prob11.center) {EOS};
\node [word,anchor=center] () at ([xshift=-0.9cm]prob11.center) {$\langle$eos$\rangle$};
\node [word,anchor=center] () at ([xshift=-0.9cm]prob21.center) {I};
\node [word,anchor=center] () at ([xshift=-0.9cm]prob31.center) {am};
\node [word,anchor=center] () at ([xshift=-0.9cm]prob41.center) {fine};
......@@ -155,7 +155,7 @@
\draw [-latex,thick] (prob\i.south) to ([yshift=-0.5cm]prob\i.south);
% Input
\node [word,anchor=south] (input1) at ([yshift=-1.8cm]prob1.south) {EOS};
\node [word,anchor=south] (input1) at ([yshift=-1.8cm]prob1.south) {$\langle$eos$\rangle$};
\node [word,anchor=south] (input2) at ([yshift=-1.8cm]prob2.south) {I};
\node [word,anchor=south] (input3) at ([yshift=-1.8cm]prob3.south) {am};
\node [word,anchor=center] (input4) at ([xshift=1cm]input3.center) {good};
......
Chapter13/Figures/figure-label-smoothing.tex
\begin{tikzpicture}
\node[font=\scriptsize,align=left] (model) at (0,0) {模型输出:\\（未使用\\标签平滑）};
\node[anchor=north west,font=\scriptsize,align=left] (label_smooth) at ([yshift=-0.3em]model.south west) {模型输出:\\（使用标\\签平滑）};
\node[anchor=south west,font=\scriptsize] (one-hot) at ([yshift=1em]model.north west) {One-hot分布:};
\node[font=\scriptsize,align=left] (model) at (0,0) {模型输出:};
\node[anchor=north west,font=\scriptsize,align=left] (label_smooth) at ([yshift=-1em]model.south west) {学习目标:\\（使用标\\签平滑）};
\node[anchor=south west,font=\scriptsize,align=left] (one-hot) at ([yshift=1em]model.north west) {标准答案:\\（未使用\\标签平滑）};
%model out
\node [anchor=west,minimum width=1.2em,minimum height=0.2em,fill=ublue!80,inner sep=0pt] (model_label1) at ([xshift=1.5em,yshift=-0.8em]model.east) {};
\node [anchor=west,minimum width=1.2em,minimum height=0.2em,fill=ublue!80,inner sep=0pt] (model_label1) at ([xshift=1.5em,yshift=-0.4em]model.east) {};
\node [anchor=south,font=\scriptsize] (model_w1) at (model_label1.north) {$p_{1}$};
\node [anchor=south west,minimum width=1.2em,minimum height=0.1em,fill=ublue!80,inner sep=0pt] (model_label2) at (model_label1.south east) {};
\node [anchor=south,font=\scriptsize] (model_w2) at (model_label2.north) {$p_{2}$};
......@@ -59,14 +59,14 @@
\node[font=\scriptsize] (line2) at ([xshift=5.9em,yshift=3em]model_label7.east) {$Loss =-\log p_{3}$};
\begin{pgfonlayer}{background}
\node [rectangle,inner sep=0.5em,rounded corners=1pt,very thick,dotted,draw=red] [fit =(model_w3) (model_label1) (model_label7)] (box1) {};
\node [rectangle,inner sep=0.5em,rounded corners=1pt,very thick,dotted,draw=red] [fit =(model_w3) (model_label1) (model_label7) (one_hot_w3)] (box1) {};
\node [rectangle,inner sep=0.2em,rounded corners=1pt,fill=purple!10,drop shadow,draw=purple] [fit = (line2)] (box3) {};
\draw [->,dotted,very thick,red] ([yshift=-0.5em]box1.east) .. controls +(east:1) and +(west:1) .. (box3.west);
\draw [->,dotted,very thick,red] ([yshift=1em]box1.east) .. controls +(east:1) and +(west:1) .. (box3.west);
\node [rectangle,inner sep=0.5em,rounded corners=1pt,very thick,dotted,draw=ugreen] [fit =(w3) (label1) (label7) ] (box2) {};
\node [rectangle,inner sep=0.7em,rounded corners=1pt,very thick,dotted,draw=ugreen] [fit =(w3) (label1) (label7) (model_w3)] (box2) {};
\node [rectangle,inner sep=0.2em,rounded corners=1pt,fill=green!10,drop shadow,draw=ugreen] [fit = (line1)] (box4) {};
\draw [->,dotted,very thick,ugreen] ([yshift=-0.5em]box2.east) .. controls +(east:1) and +(west:1) .. (box4.west);
\draw [->,dotted,very thick,ugreen] ([yshift=-1.5em]box2.east) .. controls +(east:1) and +(west:1) .. (box4.west);
\end{pgfonlayer}
......
bibliography.bib
......@@ -6619,13 +6619,13 @@ author = {Yoshua Bengio and
year = {2017}
}
@inproceedings{DBLP:journals/corr/SuGMRUVWY16a,
author = {Pei{-}Hao Su and
author = {Pei-Hao Su and
Milica Gasic and
Nikola Mrksic and
Lina Maria Rojas{-}Barahona and
Lina Maria Rojas-Barahona and
Stefan Ultes and
David Vandyke and
Tsung{-}Hsien Wen and
Tsung-Hsien Wen and
Steve J. Young},
title = {Continuously Learning Neural Dialogue Management},
publisher = {CoRR},
......@@ -6661,7 +6661,7 @@ author = {Yoshua Bengio and
Fei Tian and
Tao Qin and
Jianhuang Lai and
Tie{-}Yan Liu},
Tie-Yan Liu},
title = {A Study of Reinforcement Learning for Neural Machine Translation},
pages = {3612--3621},
publisher = {Annual Meeting of the Association for Computational Linguistics},
......@@ -9213,6 +9213,36 @@ author = {Zhuang Liu and
publisher = {{IEEE} Conference on Computer Vision and Pattern Recognition},
year = {2017}
}
@inproceedings{DBLP:conf/coling/XuHJFWHJXZ20,
author = {Chen Xu and
Bojie Hu and
Yufan Jiang and
Kai Feng and
Zeyang Wang and
Shen Huang and
Qi Ju and
Tong Xiao and
Jingbo Zhu},
title = {Dynamic Curriculum Learning for Low-Resource Neural Machine Translation},
pages = {3977--3989},
publisher = {International Conference on Computational Linguistics},
year = {2020}
}
@inproceedings{DBLP:conf/acml/WuXTZQLL18,
author = {Lijun Wu and
Yingce Xia and
Fei Tian and
Li Zhao and
Tao Qin and
Jianhuang Lai and
Tie-Yan Liu},
title = {Adversarial Neural Machine Translation},
series = {Proceedings of Machine Learning Research},
volume = {95},
pages = {534--549},
publisher = {Asian Conference on Machine Learning},
year = {2018}
}
%%%%% chapter 15------------------------------------------------------
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
......
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