Adding PC-Droid (#196)

HEPML.bib

@@ -1061,6 +1061,16 @@ @inproceedings{Rabemananjara:2023xfq
     year = "2023"
 }
 
+@article{Leigh:2023zle,
+    author = "Leigh, Matthew and Sengupta, Debajyoti and Raine, John Andrew and Qu\'etant, Guillaume and Golling, Tobias",
+    title = "{PC-Droid: Faster diffusion and improved quality for particle cloud generation}",
+    eprint = "2307.06836",
+    archivePrefix = "arXiv",
+    primaryClass = "hep-ex",
+    month = "7",
+    year = "2023"
+}
+
 
 % Jul. 12, 2023
 @article{Forestano:2023ijh,

HEPML.tex

@@ -171,7 +171,7 @@
 		\\\textit{An autoencoder consists of two functions: one that maps $x$ into a latent space $z$ (encoder) and a second one that maps the latent space back into the original space (decoder).  The encoder and decoder are simultaneously trained so that their composition is nearly the identity.  When the latent space has a well-defined probability density (as in variational autoencoders), then one can sample from the autoencoder by applying the detector to a randomly chosen element of the latent space.}
 		\item \textbf{Normalizing flows}~\cite{Albergo:2019eim,1800956,Kanwar:2003.06413,Brehmer:2020vwc,Bothmann:2020ywa,Gao:2020zvv,Gao:2020vdv,Nachman:2020lpy,Choi:2020bnf,Lu:2020npg,Bieringer:2020tnw,Hollingsworth:2021sii,Winterhalder:2021ave,Krause:2021ilc,Hackett:2021idh,Menary:2021tjg,Hallin:2021wme,NEURIPS2020_a878dbeb,Vandegar:2020yvw,Jawahar:2021vyu,Bister:2021arb,Krause:2021wez,Butter:2021csz,Winterhalder:2021ngy,Butter:2022lkf,Verheyen:2022tov,Leigh:2022lpn,Chen:2022ytr,Albandea:2022fky,Krause:2022jna,Cresswell:2022tof,Kach:2022qnf,Kach:2022uzq,Dolan:2022ikg,Backes:2022vmn,Heimel:2022wyj,Albandea:2023wgd,Rousselot:2023pcj,Diefenbacher:2023vsw,Nicoli:2023qsl,R:2023dcr,Nachman:2023clf,Raine:2023fko,Golling:2023yjq,Wen:2023oju,Xu:2023xdc,Singha:2023xxq,Buckley:2023rez,Pang:2023wfx,Golling:2023mqx,Reyes-Gonzalez:2023oei,Bickendorf:2023nej,Finke:2023ltw,Bright-Thonney:2023sqf,Albandea:2023ais,Pham:2023bnl,Gavranovic:2023oam}
 		\\\textit{Normalizing flows~\cite{pmlr-v37-rezende15} learn $p(x)$ explicitly by starting with a simple probability density and then applying a series of bijective transformations with tractable Jacobians.}
-		\item \textbf{Diffusion Models}~\cite{Mikuni:2022xry,Leigh:2023toe,Mikuni:2023dvk,Shmakov:2023kjj,Buhmann:2023bwk,Butter:2023fov,Mikuni:2023tok,Acosta:2023zik,Imani:2023blb,Amram:2023onf,Diefenbacher:2023flw,Cotler:2023lem,Diefenbacher:2023wec,Mikuni:2023tqg,Hunt-Smith:2023ccp,Buhmann:2023kdg,Buhmann:2023zgc,Buhmann:2023acn,Devlin:2023jzp}
+		\item \textbf{Diffusion Models}~\cite{Mikuni:2022xry,Leigh:2023toe,Mikuni:2023dvk,Shmakov:2023kjj,Buhmann:2023bwk,Butter:2023fov,Mikuni:2023tok,Acosta:2023zik,Leigh:2023zle,Imani:2023blb,Amram:2023onf,Diefenbacher:2023flw,Cotler:2023lem,Diefenbacher:2023wec,Mikuni:2023tqg,Hunt-Smith:2023ccp,Buhmann:2023kdg,Buhmann:2023zgc,Buhmann:2023acn,Devlin:2023jzp}
 		\\\textit{These approaches learn the gradient of the density instead of the density directly.}
 		\item \textbf{Transformer Models}~\cite{Finke:2023veq,Butter:2023fov,Raine:2023fko,Tomiya:2023jdy}
 		\\\textit{These approaches learn the density or perform generative modeling using transformer-based networks.}

README.md

@@ -1273,6 +1273,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [Jet Diffusion versus JetGPT -- Modern Networks for the LHC](https://arxiv.org/abs/2305.10475)
 * [High-dimensional and Permutation Invariant Anomaly Detection](https://arxiv.org/abs/2306.03933)
 * [Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation](https://arxiv.org/abs/2307.04780)
+* [PC-Droid: Faster diffusion and improved quality for particle cloud generation](https://arxiv.org/abs/2307.06836)
 * [Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images](https://arxiv.org/abs/2307.13687)
 * [CaloDiffusion with GLaM for High Fidelity Calorimeter Simulation](https://arxiv.org/abs/2308.03876)
 * [Refining Fast Calorimeter Simulations with a Schr\"odinger Bridge](https://arxiv.org/abs/2308.12339)

docs/index.md

@@ -1415,6 +1415,7 @@ const expandElements = shouldExpand => {
     * [Jet Diffusion versus JetGPT -- Modern Networks for the LHC](https://arxiv.org/abs/2305.10475)
     * [High-dimensional and Permutation Invariant Anomaly Detection](https://arxiv.org/abs/2306.03933)
     * [Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation](https://arxiv.org/abs/2307.04780)
+    * [PC-Droid: Faster diffusion and improved quality for particle cloud generation](https://arxiv.org/abs/2307.06836)
     * [Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images](https://arxiv.org/abs/2307.13687)
     * [CaloDiffusion with GLaM for High Fidelity Calorimeter Simulation](https://arxiv.org/abs/2308.03876)
     * [Refining Fast Calorimeter Simulations with a Schr\"odinger Bridge](https://arxiv.org/abs/2308.12339)