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12 changes: 6 additions & 6 deletions HEPML.bib
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year = "2024"
}

% October 02, 2024*
% October 02, 2024
@article{DARWIN:2024unx,
author = "{DARWIN Collaboration}",
title = "{Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline}",
Expand All @@ -465,7 +465,7 @@ @article{DARWIN:2024unx
year = "2024"
}

% October 02, 2024*
% October 02, 2024
@inproceedings{Duarte:2024lsg,
author = "Duarte, Javier M.",
title = "{Novel machine learning applications at the LHC}",
Expand All @@ -478,7 +478,7 @@ @inproceedings{Duarte:2024lsg
year = "2024"
}

% October 01, 2024*
% October 01, 2024
@article{Serhiayenka:2024han,
author = "Serhiayenka, Pavel and Roche, Stephen and Carlson, Benjamin and Hong, Tae Min",
title = "{Nanosecond hardware regression trees in FPGA at the LHC}",
Expand All @@ -490,7 +490,7 @@ @article{Serhiayenka:2024han
year = "2024"
}

% September 30, 2024*
% September 30, 2024
@article{Bhat:2024agd,
author = "Bhat, Faizan and Chowdhury, Debapriyo and Saha, Arnab Priya and Sinha, Aninda",
title = "{Bootstrapping string models with entanglement minimization and Machine-Learning}",
Expand All @@ -501,7 +501,7 @@ @article{Bhat:2024agd
year = "2024"
}

% September 27, 2024*
% September 27, 2024
@article{Chowdhury:2024ymm,
author = "Chowdhury, Talal Ahmed and Izubuchi, Taku and Kamruzzaman, Methun and Karthik, Nikhil and Khan, Tanjib and Liu, Tianbo and Paul, Arpon and Schoenleber, Jakob and Sufian, Raza Sabbir",
title = "{Polarized and unpolarized gluon PDFs: generative machine learning applications for lattice QCD matrix elements at short distance and large momentum}",
Expand All @@ -512,7 +512,7 @@ @article{Chowdhury:2024ymm
year = "2024"
}

% September 27, 2024*
% September 27, 2024
@inproceedings{Rovira:2024aqd,
author = "Rovira, M. and Parre\~no, A. and Perry, R. J.",
title = "{A Variational Approach to Quantum Field Theory}",
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12 changes: 6 additions & 6 deletions HEPML.tex
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\\\textit{Below are links to many (static) general and specialized reviews. The third bullet contains links to classic papers that applied shallow learning methods many decades before the deep learning revolution.}
\begin{itemize}
\item Modern reviews~\cite{Larkoski:2017jix,Guest:2018yhq,Albertsson:2018maf,Radovic:2018dip,Carleo:2019ptp,Bourilkov:2019yoi,Schwartz:2021ftp,Karagiorgi:2021ngt,Boehnlein:2021eym,Shanahan:2022ifi}
\item Specialized reviews~\cite{Kasieczka:2019dbj,1807719,Shlomi:2020gdn,Psihas:2020pby,Butter:2020tvl,Forte:2020yip,Brehmer:2020cvb,Nachman:2020ccu,Duarte:2020ngm,Vlimant:2020enz,Cranmer:2019eaq,Rousseau:2020rnz,Kagan:2020yrm,Guan:2020bdl,deLima:2021fwm,Alanazi:2021grv,Baldi:2022okj,Viren:2022qon,Bogatskiy:2022hub,Butter:2022rso,Dvorkin:2022pwo,Adelmann:2022ozp,Thais:2022iok,Harris:2022qtm,Coadou:2022nsh,Benelli:2022sqn,Chen:2022pzc,Plehn:2022ftl,Cheng:2022idp,Huerta:2022kgj,Huber:2022lpm,Zhou:2023pti,DeZoort:2023vrm,Du:2023qst,Allaire:2023fgp,Hashemi:2023rgo,Belis:2023mqs,Araz:2023mda,Gooding:2024wpi,Kheddar:2024osf,Bardhan:2024zla,Mondal:2024nsa,Huetsch:2024quz,Ahmad:2024dql,Barman:2024wfx,Larkoski:2024uoc,Halverson:2024hax,Sahu:2024fzi}
\item Specialized reviews~\cite{Kasieczka:2019dbj,1807719,Shlomi:2020gdn,Psihas:2020pby,Butter:2020tvl,Forte:2020yip,Brehmer:2020cvb,Nachman:2020ccu,Duarte:2020ngm,Vlimant:2020enz,Cranmer:2019eaq,Rousseau:2020rnz,Kagan:2020yrm,Guan:2020bdl,deLima:2021fwm,Alanazi:2021grv,Baldi:2022okj,Viren:2022qon,Bogatskiy:2022hub,Butter:2022rso,Dvorkin:2022pwo,Adelmann:2022ozp,Thais:2022iok,Harris:2022qtm,Coadou:2022nsh,Benelli:2022sqn,Chen:2022pzc,Plehn:2022ftl,Cheng:2022idp,Huerta:2022kgj,Huber:2022lpm,Zhou:2023pti,DeZoort:2023vrm,Du:2023qst,Allaire:2023fgp,Hashemi:2023rgo,Belis:2023mqs,Araz:2023mda,Gooding:2024wpi,Kheddar:2024osf,Bardhan:2024zla,Mondal:2024nsa,Huetsch:2024quz,Ahmad:2024dql,Barman:2024wfx,Larkoski:2024uoc,Halverson:2024hax,Sahu:2024fzi,Duarte:2024lsg}
\item Classical papers~\cite{Denby:1987rk,Lonnblad:1990bi}
\item Datasets~\cite{Kasieczka:2021xcg,Aarrestad:2021oeb,Benato:2021olt,Govorkova:2021hqu,Chen:2021euv,Qu:2022mxj,Eller:2023myr,Rusack:2023pob,Zoch:2024eyp}
\end{itemize}
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\begin{itemize}
\item \textbf{Software}~\cite{Strong:2020mge,Gligorov:2012qt,Weitekamp:DLPS2017,Nguyen:2018ugw,Bourgeois:2018nvk,1792136,Balazs:2021uhg,Rehm:2021zow,Mahesh:2021iph,Amrouche:2021tio,Pol:2021iqw,Goncharov:2021wvd,Saito:2021vpp,Jiang:2022zho,Garg:2022tal,Duarte:2022job,Guo:2023nfu,Tyson:2023zkx,DPHEP:2023blx,DiBello:2023kzc,Bal:2023bvt,Kauffman:2024bov,Held:2024gwj,CALICE:2024imr,Ivanov:2024whr,Bierlich:2024vqo,Pratiush:2024ltm}
\\\textit{Strategies for efficient inference for a given hardware architecture.}
\item \textbf{Hardware/firmware}~\cite{Duarte:2018ite,DiGuglielmo:2020eqx,Summers:2020xiy,1808088,Iiyama:2020wap,Mohan:2020vvi,Carrazza:2020qwu,Rankin:2020usv,Heintz:2020soy,Rossi:2020sbh,Aarrestad:2021zos,Hawks:2021ruw,Teixeira:2021yhl,Hong:2021snb,DiGuglielmo:2021ide,Migliorini:2021fuj,Govorkova:2021utb,Elabd:2021lgo,Jwa:2019zlh,Butter:2022lkf,Sun:2022bxx,Khoda:2022dwz,Carlson:2022vac,Abidi:2022ogh,MeyerzuTheenhausen:2022ffb,Cai:2023ldc,Herbst:2023lug,Coccaro:2023nol,Neu:2023sfh,Okabe:2023efz,Yaary:2023dvw,Schulte:2023gtt,Yoo:2023lxy,Grosso:2023owo,Jin:2023xts,Lin:2023xrw,Zipper:2023ybp,Delaney:2023swp,Dickinson:2023yes,CMS:2024twn,Bahr:2024dzg,Tiras:2024yzr,Parpillon:2024maz,Los:2024xzl,Zhu:2024ubz,Borella:2024mgs}
\item \textbf{Hardware/firmware}~\cite{Duarte:2018ite,DiGuglielmo:2020eqx,Summers:2020xiy,1808088,Iiyama:2020wap,Mohan:2020vvi,Carrazza:2020qwu,Rankin:2020usv,Heintz:2020soy,Rossi:2020sbh,Aarrestad:2021zos,Hawks:2021ruw,Teixeira:2021yhl,Hong:2021snb,DiGuglielmo:2021ide,Migliorini:2021fuj,Govorkova:2021utb,Elabd:2021lgo,Jwa:2019zlh,Butter:2022lkf,Sun:2022bxx,Khoda:2022dwz,Carlson:2022vac,Abidi:2022ogh,MeyerzuTheenhausen:2022ffb,Cai:2023ldc,Herbst:2023lug,Coccaro:2023nol,Neu:2023sfh,Okabe:2023efz,Yaary:2023dvw,Schulte:2023gtt,Yoo:2023lxy,Grosso:2023owo,Jin:2023xts,Lin:2023xrw,Zipper:2023ybp,Delaney:2023swp,Dickinson:2023yes,CMS:2024twn,Bahr:2024dzg,Tiras:2024yzr,Parpillon:2024maz,Los:2024xzl,Zhu:2024ubz,Borella:2024mgs,Serhiayenka:2024han}
\\\textit{Various accelerators have been studied for fast inference that is very important for latency-limited applications like the trigger at collider experiments.}
\item \textbf{Deployment}~\cite{Kuznetsov:2020mcj,SunnebornGudnadottir:2021nhk,Holmberg:2023rfr,Savard:2023wwi,Bieringer:2024pzt,Li:2024uju}
\\\textit{This category is for the deployment of machine learning interfaces, such as in the cloud.}
Expand All @@ -147,7 +147,7 @@
\\\textit{Regression methods can be used as surrogate models for functions that are too slow to evaluate. One important class of functions are matrix elements, which form the core component of cross section calculations in quantum field theory.}
\item \textbf{Parameter estimation}~\cite{Lei:2020ucb,1808105,Lazzarin:2020uvv,Kim:2021pcz,Alda:2021rgt,Craven:2021ems,Castro:2022zpq,Meng:2022lmd,Garg:2022tal,Qiu:2023ihi,AlHammal:2023svo,Shi:2023xfz,Goos:2023opq,Schroder:2023akt,Yang:2023rbg,Dubey:2023pro,Simkina:2023ztj,Biro:2024tzv}
\\\textit{The target features could be parameters of a model, which can be learned directly through a regression setup. Other forms of inference are described in later sections (which could also be viewed as regression).}
\item \textbf{Parton Distribution Functions (and related)}~\cite{DelDebbio:2020rgv,Grigsby:2020auv,Rossi:2020sbh,Carrazza:2021hny,Ball:2021leu,Ball:2021xlu,Khalek:2021gon,Iranipour:2022iak,Gao:2022uhg,Gao:2022srd,Candido:2023utz,Wang:2023nab,Kassabov:2023hbm,Wang:2023poi,Fernando:2023obn,Rabemananjara:2023xfq,Kriesten:2023uoi,NNPDF:2024djq,NNPDF:2024dpb,DallOlio:2024vjv,Gombas:2024rvw,Costantini:2024xae,Bertone:2024taw,Soleymaninia:2024jam,Ochoa-Oregon:2024zgm,Barontini:2024dyb,Yan:2024yir,Liuti:2024umy,Kriesten:2024are}
\item \textbf{Parton Distribution Functions (and related)}~\cite{DelDebbio:2020rgv,Grigsby:2020auv,Rossi:2020sbh,Carrazza:2021hny,Ball:2021leu,Ball:2021xlu,Khalek:2021gon,Iranipour:2022iak,Gao:2022uhg,Gao:2022srd,Candido:2023utz,Wang:2023nab,Kassabov:2023hbm,Wang:2023poi,Fernando:2023obn,Rabemananjara:2023xfq,Kriesten:2023uoi,NNPDF:2024djq,NNPDF:2024dpb,DallOlio:2024vjv,Gombas:2024rvw,Costantini:2024xae,Bertone:2024taw,Soleymaninia:2024jam,Ochoa-Oregon:2024zgm,Barontini:2024dyb,Yan:2024yir,Liuti:2024umy,Kriesten:2024are,Chowdhury:2024ymm}
\\\textit{Various machine learning models can provide flexible function approximators, which can be useful for modeling functions that cannot be determined easily from first principles such as parton distribution functions.}
\item \textbf{Lattice Gauge Theory}~\cite{Kanwar:2003.06413,Favoni:2020reg,Bulusu:2021rqz,Shi:2021qri,Hackett:2021idh,Yoon:2018krb,Zhang:2019qiq,Nguyen:2019gpo,Favoni:2021epq,Chen:2021jey,Bulusu:2021njs,Shi:2022yqw,Luo:2022jzl,Chen:2022ytr,Li:2022ozl,Kang:2022jbg,Albandea:2022fky,Khan:2022vot,Sale:2022snt,Kim:2022rna,Karsch:2022yka,Favoni:2022mcg,Chen:2022asj,Bacchio:2022vje,Bacchio:2022vje,Gao:2022uhg,Aguilar:2022thg,Lawrence:2022dba,Peng:2022wdl,Lehner:2023bba,Albandea:2023wgd,Nicoli:2023qsl,Aronsson:2023rli,Zhou:2023pti,Hudspith:2023loy,R:2023dcr,Bender:2023gwr,NarcisoFerreira:2023kak,Lehner:2023prf,Singha:2023xxq,Riberdy:2023awf,Buzzicotti:2023qdv,Caselle:2023mvh,Detmold:2023kjm,Kashiwa:2023dfx,Ermann:2023unw,Albandea:2023ais,Alvestad:2023jgl,Tomiya:2023jdy,Wang:2023sry,Gao:2023uel,Soloveva:2023tvj,Holland:2023lfx,Gao:2023quv,Foreman:2023ymy,Lawrence:2023cft,Kanwar:2024ujc,Goswami:2024jlc,Holland:2024muu,Catumba:2024wxc,Chen:2024ckb,Boyle:2024nlh,Chu:2024swv,Bonanno:2024udh,Lin:2024eiz,Kim:2024rpd,Finkenrath:2024tdp,Abbott:2024knk,Bai:2024pii,Chen:2024mmd,Xu:2024tjp,Apte:2024vwn,Bachtis:2024vks,Cai:2024eqa,Jiang:2024vsr,Bachtis:2024dss,Gao:2024nzg,Luo:2024iwf,Gao:2024zdz,Rovira:2024aqd}
\\\textit{Lattice methods offer a complementary approach to perturbation theory. A key challenge is to create approaches that respect the local gauge symmetry (equivariant networks).}
Expand Down Expand Up @@ -186,7 +186,7 @@
\item \textbf{Other/hybrid}~\cite{Cresswell:2022tof,DiBello:2022rss,Li:2022jon,Kansal:2022spb,Butter:2023fov,Kronheim:2023jrl,Santos:2023mib,Sahu:2023uwb}
\\\textit{Architectures that combine different network elements or otherwise do not fit into the other categories.}
\end{itemize}
\item \textbf{Anomaly detection}~\cite{DAgnolo:2018cun,Collins:2018epr,Collins:2019jip,DAgnolo:2019vbw,Farina:2018fyg,Heimel:2018mkt,Roy:2019jae,Cerri:2018anq,Blance:2019ibf,Hajer:2018kqm,DeSimone:2018efk,Mullin:2019mmh,1809.02977,Dillon:2019cqt,Andreassen:2020nkr,Nachman:2020lpy,Aguilar-Saavedra:2017rzt,Romao:2019dvs,Romao:2020ojy,knapp2020adversarially,collaboration2020dijet,1797846,1800445,Amram:2020ykb,Cheng:2020dal,Khosa:2020qrz,Thaprasop:2020mzp,Alexander:2020mbx,aguilarsaavedra2020mass,1815227,pol2020anomaly,Mikuni:2020qds,vanBeekveld:2020txa,Park:2020pak,Faroughy:2020gas,Stein:2020rou,Kasieczka:2021xcg,Chakravarti:2021svb,Batson:2021agz,Blance:2021gcs,Bortolato:2021zic,Collins:2021nxn,Dillon:2021nxw,Finke:2021sdf,Shih:2021kbt,Atkinson:2021nlt,Kahn:2021drv,Aarrestad:2021oeb,Dorigo:2021iyy,Caron:2021wmq,Govorkova:2021hqu,Kasieczka:2021tew,Volkovich:2021txe,Govorkova:2021utb,Hallin:2021wme,Ostdiek:2021bem,Fraser:2021lxm,Jawahar:2021vyu,Herrero-Garcia:2021goa,Aguilar-Saavedra:2021utu,Tombs:2021wae,Lester:2021aks,Mikuni:2021nwn,Chekanov:2021pus,dAgnolo:2021aun,Canelli:2021aps,Ngairangbam:2021yma,Bradshaw:2022qev,Aguilar-Saavedra:2022ejy,Buss:2022lxw,Alvi:2022fkk,Jiang:2022sfw,Dillon:2022tmm,Birman:2022xzu,Raine:2022hht,Letizia:2022xbe,Fanelli:2022xwl,Finke:2022lsu,Verheyen:2022tov,Dillon:2022mkq,Caron:2022wrw,Park:2022zov,Kamenik:2022qxs,Hallin:2022eoq,Kasieczka:2022naq,Araz:2022zxk,Mastandrea:2022vas,Schuhmacher:2023pro,Roche:2023int,Golling:2023juz,Sengupta:2023xqy,Mikuni:2023tok,Golling:2023yjq,Vaslin:2023lig,ATLAS:2023azi,Chekanov:2023uot,CMSECAL:2023fvz,Bickendorf:2023nej,Finke:2023ltw,Buhmann:2023acn,Freytsis:2023cjr,Grosso:2023owo,Bai:2023yyy,Zhang:2023khv,Liu:2023djx,Metodiev:2023izu,Zipper:2023ybp,Sengupta:2023vtm,Krause:2023uww,Cheng:2024yig,Li:2024htp,Grosso:2024nho,Leigh:2024chm,Harilal:2024tqq,Matos:2024ggs,Chekanov:2024ezm,Zhang:2024ebl}
\item \textbf{Anomaly detection}~\cite{DAgnolo:2018cun,Collins:2018epr,Collins:2019jip,DAgnolo:2019vbw,Farina:2018fyg,Heimel:2018mkt,Roy:2019jae,Cerri:2018anq,Blance:2019ibf,Hajer:2018kqm,DeSimone:2018efk,Mullin:2019mmh,1809.02977,Dillon:2019cqt,Andreassen:2020nkr,Nachman:2020lpy,Aguilar-Saavedra:2017rzt,Romao:2019dvs,Romao:2020ojy,knapp2020adversarially,collaboration2020dijet,1797846,1800445,Amram:2020ykb,Cheng:2020dal,Khosa:2020qrz,Thaprasop:2020mzp,Alexander:2020mbx,aguilarsaavedra2020mass,1815227,pol2020anomaly,Mikuni:2020qds,vanBeekveld:2020txa,Park:2020pak,Faroughy:2020gas,Stein:2020rou,Kasieczka:2021xcg,Chakravarti:2021svb,Batson:2021agz,Blance:2021gcs,Bortolato:2021zic,Collins:2021nxn,Dillon:2021nxw,Finke:2021sdf,Shih:2021kbt,Atkinson:2021nlt,Kahn:2021drv,Aarrestad:2021oeb,Dorigo:2021iyy,Caron:2021wmq,Govorkova:2021hqu,Kasieczka:2021tew,Volkovich:2021txe,Govorkova:2021utb,Hallin:2021wme,Ostdiek:2021bem,Fraser:2021lxm,Jawahar:2021vyu,Herrero-Garcia:2021goa,Aguilar-Saavedra:2021utu,Tombs:2021wae,Lester:2021aks,Mikuni:2021nwn,Chekanov:2021pus,dAgnolo:2021aun,Canelli:2021aps,Ngairangbam:2021yma,Bradshaw:2022qev,Aguilar-Saavedra:2022ejy,Buss:2022lxw,Alvi:2022fkk,Jiang:2022sfw,Dillon:2022tmm,Birman:2022xzu,Raine:2022hht,Letizia:2022xbe,Fanelli:2022xwl,Finke:2022lsu,Verheyen:2022tov,Dillon:2022mkq,Caron:2022wrw,Park:2022zov,Kamenik:2022qxs,Hallin:2022eoq,Kasieczka:2022naq,Araz:2022zxk,Mastandrea:2022vas,Schuhmacher:2023pro,Roche:2023int,Golling:2023juz,Sengupta:2023xqy,Mikuni:2023tok,Golling:2023yjq,Vaslin:2023lig,ATLAS:2023azi,Chekanov:2023uot,CMSECAL:2023fvz,Bickendorf:2023nej,Finke:2023ltw,Buhmann:2023acn,Freytsis:2023cjr,Grosso:2023owo,Bai:2023yyy,Zhang:2023khv,Liu:2023djx,Metodiev:2023izu,Zipper:2023ybp,Sengupta:2023vtm,Krause:2023uww,Cheng:2024yig,Li:2024htp,Grosso:2024nho,Leigh:2024chm,Harilal:2024tqq,Matos:2024ggs,Chekanov:2024ezm,Zhang:2024ebl,Duarte:2024lsg,DARWIN:2024unx}
\\\textit{The goal of anomaly detection is to identify abnormal events. The abnormal events could be from physics beyond the Standard Model or from faults in a detector. While nearly all searches for new physics are technically anomaly detection, this category is for methods that are mode-independent (broadly defined). Anomalies in high energy physics tend to manifest as over-densities in phase space (often called `population anomalies') in contrast to off-manifold anomalies where you can flag individual examples as anomalous. }
\item \textbf{Foundation Models, LLMs}~\cite{Vigl:2024lat,Birk:2024knn,Harris:2024sra,Fanelli:2024ktq,Zhang:2024kws,Mikuni:2024qsr,Leigh:2024ked}
\\\textit{A foundation model is a machine learning or deep learning model that is trained on broad data such that it can be applied across a wide range of use cases.}
Expand All @@ -195,7 +195,7 @@
\begin{itemize}
\item \textbf{Parameter estimation}~\cite{Andreassen:2019nnm,Stoye:2018ovl,Hollingsworth:2020kjg,Brehmer:2018kdj,Brehmer:2018eca,Brehmer:2019xox,Brehmer:2018hga,Cranmer:2015bka,Andreassen:2020gtw,Coogan:2020yux,Flesher:2020kuy,Bieringer:2020tnw,Nachman:2021yvi,Chatterjee:2021nms,NEURIPS2020_a878dbeb,Mishra-Sharma:2021oxe,Barman:2021yfh,Bahl:2021dnc,Arganda:2022qzy,Kong:2022rnd,Arganda:2022zbs,Butter:2022vkj,Neubauer:2022gbu,Rizvi:2023mws,Heinrich:2023bmt,Breitenmoser:2023tmi,Erdogan:2023uws,Morandini:2023pwj,Barrue:2023ysk,Espejo:2023wzf,Heimel:2023mvw,Chai:2024zyl,Chatterjee:2024pbp,Alvarez:2024owq,Diaz:2024yfu,Mastandrea:2024irf,JETSCAPE:2024cqe}
\\\textit{This can also be viewed as a regression problem, but there the goal is typically to do maximum likelihood estimation in contrast to directly minimizing the mean squared error between a function and the target.}
\item \textbf{Unfolding}~\cite{Mieskolainen:2018fhf,Andreassen:2019cjw,Datta:2018mwd,Bellagente:2019uyp,Gagunashvili:2010zw,Glazov:2017vni,Martschei:2012pr,Lindemann:1995ut,Zech2003BinningFreeUB,1800956,Vandegar:2020yvw,Howard:2021pos,Baron:2021vvl,Andreassen:2021zzk,Komiske:2021vym,H1:2021wkz,Arratia:2021otl,Wong:2021zvv,Arratia:2022wny,Backes:2022vmn,Chan:2023tbf,Shmakov:2023kjj,Shmakov:2024gkd,Huetsch:2024quz,Desai:2024kpd,Zhu:2024drd}
\item \textbf{Unfolding}~\cite{Mieskolainen:2018fhf,Andreassen:2019cjw,Datta:2018mwd,Bellagente:2019uyp,Gagunashvili:2010zw,Glazov:2017vni,Martschei:2012pr,Lindemann:1995ut,Zech2003BinningFreeUB,1800956,Vandegar:2020yvw,Howard:2021pos,Baron:2021vvl,Andreassen:2021zzk,Komiske:2021vym,H1:2021wkz,Arratia:2021otl,Wong:2021zvv,Arratia:2022wny,Backes:2022vmn,Chan:2023tbf,Shmakov:2023kjj,Shmakov:2024gkd,Huetsch:2024quz,Desai:2024kpd,Zhu:2024drd,Duarte:2024lsg}
\\\textit{This is the task of removing detector distortions. In contrast to parameter estimation, the goal is not to infer model parameters, but instead, the undistorted phase space probability density. This is often also called deconvolution.}
\item \textbf{Domain adaptation}~\cite{Rogozhnikov:2016bdp,Andreassen:2019nnm,Cranmer:2015bka,Diefenbacher:2020rna,Nachman:2021opi,Camaiani:2022kul,Schreck:2023pzs,Algren:2023qnb,Zhao:2024ely,Kelleher:2024rmb,Kelleher:2024jsh,Glazier:2024ogg}
\\\textit{Morphing simulations to look like data is a form of domain adaptation.}
Expand All @@ -220,7 +220,7 @@
\\\textit{ML can also be utilized in formal theory.}
\begin{itemize}
\item Theory and physics for ML~\cite{Erbin:2022lls,Zuniga-Galindo:2023hty,Banta:2023kqe,Zuniga-Galindo:2023uwp,Kumar:2023hlu,Demirtas:2023fir,Halverson:2023ndu,Zhang:2024mcu}
\item ML for theory~\cite{Berglund:2022gvm,Erbin:2022rgx,Gerdes:2022nzr,Escalante-Notario:2022fik,Chen:2022jwd,Cheung:2022itk,He:2023csq,Lal:2023dkj,Dorrill:2023vox,Forestano:2023ijh,Dersy:2023job,Cotler:2023lem,Mizera:2023bsw,Gnech:2023prs,Seong:2023njx,Wojcik:2023usm,Alawadhi:2023gxa,Choi:2023rqg,Halverson:2023ndu,Matchev:2023mii,Lanza:2023vee,Erbin:2023ncy,Hirst:2023kdl,Ishiguro:2023hcv,Constantin:2024yxh,Berman:2024pax,Gukov:2024buj,Lanza:2024mqp,Hashimoto:2024aga,Orman:2024mpw,Bea:2024xgv,Balduf:2024gvv,Hou:2024vtx,Keita:2024skh,LopesCardoso:2024tol,Gukov:2024opc,Dao:2024zab,Cheung:2024svk,Bodendorfer:2024egw,Halverson:2024axc,Capuozzo:2024vdw}
\item ML for theory~\cite{Berglund:2022gvm,Erbin:2022rgx,Gerdes:2022nzr,Escalante-Notario:2022fik,Chen:2022jwd,Cheung:2022itk,He:2023csq,Lal:2023dkj,Dorrill:2023vox,Forestano:2023ijh,Dersy:2023job,Cotler:2023lem,Mizera:2023bsw,Gnech:2023prs,Seong:2023njx,Wojcik:2023usm,Alawadhi:2023gxa,Choi:2023rqg,Halverson:2023ndu,Matchev:2023mii,Lanza:2023vee,Erbin:2023ncy,Hirst:2023kdl,Ishiguro:2023hcv,Constantin:2024yxh,Berman:2024pax,Gukov:2024buj,Lanza:2024mqp,Hashimoto:2024aga,Orman:2024mpw,Bea:2024xgv,Balduf:2024gvv,Hou:2024vtx,Keita:2024skh,LopesCardoso:2024tol,Gukov:2024opc,Dao:2024zab,Cheung:2024svk,Bodendorfer:2024egw,Halverson:2024axc,Capuozzo:2024vdw,Bhat:2024agd}
\end{itemize}
\item \textbf{Experimental results}
\\\textit{This section is incomplete as there are many results that directly and indirectly (e.g. via flavor tagging) use modern machine learning techniques. We will try to highlight experimental results that use deep learning in a critical way for the final analysis sensitivity.}
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