Fix Classification section

HEPML.tex

@@ -47,8 +47,8 @@
 \item \textbf{Classification}
 \\\textit{Given a feature space $x\in\mathbb{R}^n$, a binary classifier is a function $f:\mathbb{R}^n\rightarrow [0,1]$, where $0$ corresponds to features that are more characteristic of the zeroth class (e.g. background) and $1$ correspond to features that are more characteristic of the one class (e.g. signal).  Typically, $f$ will be a function specified by some parameters $w$ (e.g. weights and biases of a neural network) that are determined by minimizing a loss of the form $L[f]=\sum_{i}\ell(f(x_i),y_i)$, where $y_i\in\{0,1\}$ are labels.  The function $\ell$ is smaller when $f(x_i)$ and $y_i$ are closer.  Two common loss functions are the mean squared error $\ell(x,y)=(x-y)^2$ and the binary cross entropy $\ell(x,y)=y\log(x)+(1-y)\log(1-x)$.  Exactly what `more characteristic of' means depends on the loss function used to determine $f$.  It is also possible to make a multi-class classifier.  A common strategy for the multi-class case is to represent each class as a different basis vector in $\mathbb{R}^{n_\text{classes}}$ and then $f(x)\in[0,1]^{n_\text{classes}}$.  In this case, $f(x)$ is usually restricted to have its $n_\text{classes}$ components sum to one and the loss function is typically the cross entropy $\ell(x,y)=\sum_\text{classes $i$} y_i\log(x)$.}
 	\begin{itemize}
-		\item \textbf{Parameterized classifiers}~\cite{Baldi:2016fzo,Cranmer:2015bka,Nachman:2021yvi}.
-			\\\textit{A classifier that is conditioned on model parameters $f(x|\theta)$ is called a parameterized classifier.}
+		\item \textbf{Parameterized classifiers}~\cite{Baldi:2016fzo,Cranmer:2015bka,Nachman:2021yvi}
+		\\\textit{A classifier that is conditioned on model parameters $f(x|\theta)$ is called a parameterized classifier.}
 		\item \textbf{Representations}
 			\\\textit{There is no unique way to represent high energy physics data.  It is often natural to encode $x$ as an image or another one of the structures listed below.}
 			\begin{itemize}
@@ -67,7 +67,7 @@
 				\item \textbf{Physics-inspired basis}~\cite{Datta:2019,Datta:2017rhs,Datta:2017lxt,Komiske:2017aww,Butter:2017cot,Grojean:2020ech}
 				\\\textit{This is a catch-all category for learning using other representations that use some sort of manual or automated physics-preprocessing.}
 			\end{itemize}
-		\item Targets
+		\item \textbf{Targets}
 			\begin{itemize}
 			\item \textbf{$W/Z$ tagging}~\cite{deOliveira:2015xxd,Barnard:2016qma,Louppe:2017ipp,Sirunyan:2020lcu,Chen:2019uar,1811770,Dreyer:2020brq,Kim:2021gtv}
 			\\\textit{Boosted, hadronically decaying $W$ and $Z$ bosons form jets that are distinguished from generic quark and gluon jets by their mass near the boson mass and their two-prong substructure.}

README.md

@@ -74,6 +74,8 @@ The purpose of this note is to collect references for modern machine learning as
         * [Approximating Likelihood Ratios with Calibrated Discriminative  Classifiers](https://arxiv.org/abs/1506.02169)
         * [E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once](https://arxiv.org/abs/2101.07263)
 
+    *  Representations
+
         *  Jet images
 
             * [How to tell quark jets from gluon jets](https://doi.org/10.1103/PhysRevD.44.2025)
@@ -190,6 +192,8 @@ The purpose of this note is to collect references for modern machine learning as
             * [Deep-learned Top Tagging with a Lorentz Layer](https://arxiv.org/abs/1707.08966) [[DOI](https://doi.org/10.21468/SciPostPhys.5.3.028)]
             * [Resurrecting $b\bar{b}h$ with kinematic shapes](https://arxiv.org/abs/2011.13945)
 
+    *  Targets
+
         *  $W/Z$ tagging
 
             * [Jet-images — deep learning edition](https://arxiv.org/abs/1511.05190) [[DOI](https://doi.org/10.1007/JHEP07(2016)069)]
@@ -486,6 +490,8 @@ The purpose of this note is to collect references for modern machine learning as
             * [Estimating elliptic flow coefficient in heavy ion collisions using deep learning](https://arxiv.org/abs/2203.01246) [[DOI](https://doi.org/10.1103/PhysRevD.105.114022)]
             * [Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies](https://arxiv.org/abs/2301.10426)
 
+    *  Learning strategies
+
         *  Hyperparameters
 
             * [Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics](https://arxiv.org/abs/2011.04434) [[DOI](https://doi.org/10.1140/epjc/s10052-021-08950-y)]
@@ -585,6 +591,8 @@ The purpose of this note is to collect references for modern machine learning as
             * [Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport](https://arxiv.org/abs/2208.02807)
             * [Optimal transport for a global event description at high-intensity hadron colliders](https://arxiv.org/abs/2211.02029)
 
+    *  Fast inference / deployment
+
         *  Software
 
             * [On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics](https://arxiv.org/abs/2002.01427) [[DOI](https://doi.org/10.1088/2632-2153/ab983a)]

make_md.py

@@ -181,7 +181,7 @@ def convert_from_bib(myline):
                     myfile_out.write("\n")
                     pass
                 pass
-            elif "cite" in line:
+            else:
                 mybuffer = ""
                 for j in range(itemize_counter-1):
                      mybuffer+="    "
@@ -193,12 +193,14 @@ def convert_from_bib(myline):
                         myfile_out.write(mybuffer+"    * "+convert_from_bib(cite)+"\n")
                         pass
                     myfile_out.write("\n")
-                else:
+                elif "cite" in line:
                     myfile_out.write(mybuffer+"* "+line.split(r"~\cite{")[0].split(r"\item")[1]+"\n\n")
                     mycites = line.split(r"~\cite{")[1].split("}")[0].split(",")
                     for cite in mycites:
                         myfile_out.write(mybuffer+"    * "+convert_from_bib(cite)+"\n")
                         pass
                     myfile_out.write("\n")
                     pass
+                else:
+                    myfile_out.write(mybuffer+"* "+line.split(r"\item")[1]+"\n\n")
                 pass