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AutomL: A systematic review on automated machine learning with neural architecture search

Journal of Information and Intelligence 2024年第1期2卷 52-81页

作者： Imrus Salehin md.Shamiul Islam Pritom Saha S.m.Noman Azra Tuni md.mehedi Hasan md.Abu Baten Department of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan 47011Republic of Korea Department of Computer Science and Engineering Daffodil International UniversityDhaka 1216Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and TechnologyDhaka 1216Bangladesh Faculty of Computer Science and Engineering Frankfurt University of Applied SciencesFrankfurt 60318Germany Department of Computer Science and Engineering University of Asia PacificDhaka 1205Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka 1208Bangladesh Department of Computer Science and Engineering Northern University BangladeshDhaka 1215Bangladesh

AutomL(Automated machine Learning)is an emerging field that aims to automate the process of building machine learning *** emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is *** particular,research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and *** this semantic review research,we summarize the data processing requirements for AutomL approaches and provide a detailed *** place greater emphasis on neural architecture search(nas)as it currently represents a highly popular sub-topic within the field of *** methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given *** provide a summary of the performance achieved by representative nas algorithms on the CIFAR-10,CIFAR-100,ImageNet and wellknown benchmark ***,we delve into several noteworthy research directions in nas.methods including one/two-stage nas,one-shot nas and joint hyperparameter with architecture *** discussed how the search space size and complexity in nas can vary depending on the specific problem being *** conclude,we examine several open problems(SOTA problems)within current AutomL methods that assure further investigation in future research.

关键词： AutomL Neural architecture search Advance machine learning Search space Hyperparameter optimization

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Search for the doubly charmed baryon ■

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Science China(Physics,mechanics & Astronomy) 2020年第2期63卷 8-22页

作者： R.Aaij C.Abellán Beteta T.Ackernley B.Adeva m.Adinolfi H.Afsharnia C.A.Aidala S.Aiola Z.Ajaltouni S.Akar P.Albicocco J.Albrecht F.Alessio m.Alexander A.Alfonso Albero G.Alkhazov P.Alvarez Cartelle A.A.Alves Jr S.Amato Y.Amhis L.An L.Anderlini G.Andreassi m.Andreotti F.Archilli J.Arnau Romeu A.Artamonov m.Artuso K.Arzymatov E.Aslanides m.Atzeni B.Audurier S.Bachmann J.J.Back S.Baker V.Balagura W.Baldini A.Baranov R.J.Barlow S.Barsuk W.Barter m.Bartolini F.Baryshnikov G.Bassi V.Batozskaya B.Batsukh A.Battig V.Battista A.Bay m.Becker F.Bedeschi I.Bediaga A.Beiter L.J.Bel V.Belavin S.Belin N.Beliy V.Bellee K.Belous I.Belyaev G.Bencivenni E.Ben-Haim S.Benson S.Beranek A.Berezhnoy *** D.Berninghoff H.C.Bernstein E.Bertholet A.Bertolin C.Betancourt F.Betti m.O.Bettler Ia.Bezshyiko S.Bhasin J.Bhom m.S.Bieker S.Bifani P.Billoir A.Birnkraut A.Bizzeti m.Bjφrn m.P.Blago T.Blake F.Blanc S.Blusk D.Bobulska V.Bocci O.Boente Garcia T.Boettcher A.Boldyrev A.Bondar N.Bondar S.Borghi m.Borisyak m.Borsato J.T.Borsuk T.J.V.Bowcock C.Bozzi S.Braun A.Brea Rodriguez m.Brodski J.Brodzicka A.Brossa Gonzalo D.Brundu E.Buchanan A.Buonaura C.Burr A.Bursche J.S.Butter J.Buytaert W.Byczynski S.Cadeddu H.Cai R.Calabrese S.Cali R.Calladine m.Calvi m.Calvo Gomez A.Camboni P.Campana D.H.Campora Perez L.Capriotti A.Carbone G.Carboni R.Cardinale A.Cardini P.Carniti K.Carvalho Akiba A.Casais Vidal G.Casse m.Cattaneo G.Cavallero R.Cenci J.Cerasoli m.G.Chapman m.Charles Ph.Charpentier G.Chatzikonstantinidis m.Chefdeville V.Chekalina C.Chen S.Chen A.Chernov S.-G.Chitic V.Chobanova m.Chrzaszcz A.Chubykin P.Ciambrone m.F.Cicala X.Cid Vidal G.Ciezarek F.Cindolo P.E.L.Clarke m.Clemencic H.V.Cliff J.Closier J.L.Cobbledick V.Coco J.A.B.Coelho J.Cogan E.Cogneras L.Cojocariu P.Collins T.Colombo A.Comerma-montells A.Contu N.Cooke G.Coombs S.Coquereau G.Corti C.m.Costa Sobral B.Couturier D.C.Craik J.Crkovska A.Crocombe m.Cruz Torres R.Currie C.L.Da Silva E.Dall'Occo J.Dalseno C.D'Ambrosio A.Danilina P.d'Argent A.Davis O.De Aguiar Francisco K.De Bruyn S.De Capua m Centro Brasileiro de Pesquisas Fisicas(CBPF) Rio de JaneiroBrazil Universidade Federal do Rio de Janeiro(UFRJ) Rio de JaneiroBrazil Center for High Energy Physics Tsinghua UniversityBeijingChina School of Physics State Key Laboratory of Nuclear Physics and Technology Peking UniversityBeijingChina University of Chinese Academy of Sciences BeijingChina Institute Of High Energy Physics(IHEP) BeijingChina Institute of Particle Physics Central China Normal UniversityWuhanHubeiChina Univ.Grenoble Alpes Univ.Savoie Mont BlancCNRSIN2P3-LAPPAnnecyFrance Universite Clermont Auvergne CNRS/IN2P3LPCClermont-FerrandFrance Aix marseille Univ CNRS/IN2P3CPPMMarseilleFrance LAL Univ.Paris-SudCNRS/IN2P3Universite Paris-SaclayOrsayFrance LPNHE Sorbonne Universite Paris Diderot Sorbonne Paris Cite CNRS/IN2P3ParisFrance I.Physikalisches Institut RWTH Aachen UniversityAachenGermany Fakultat Physik Technische Universitat DortmundDortmundGermany max-Planck-Institut für Kernphysik(mPIK) HeidelbergGermany Physikalisches Institut Ruprecht-Karls-Universitat HeidelbergHeidelbergGermany School of Physics University College DublinDublinIreland INFN Sezione di Bari BariItaly INFN Sezione di Bologna BolognaItaly INFN Sezione di Ferrara FerraraItaly INFN Sezione di Firenze FirenzeItaly INFN Laboratori Nazionali di Frascati FrascatiItaly INFN Sezione di Genova GenovaItaly INFN Sezione di milano-Bicocca MilanoItaly INFN Sezione di milano MilanoItaly INFN Sezione di Cagliari MonserratoItaly INFN Sezione di Padova PadovaItaly INFN Sezione di Pisa PisaItaly INFN Sezione di Roma Tor Vergata RomaItaly INFN Sezione di Roma La Sapienza RomaItaly Nikhef National Institute for Subatomic Physics AmsterdamNetherlands Nikhef National Institute for Subatomic Physics and VU University Amsterdam AmsterdamNetherlands Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences KrakowPoland AGH-University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakowPoland Natio

A search for the doubly charmed baryon ■^+cc is performed through its decay to theΛ^+c K^-π^+ final state,using proton-proton collision data collected with the LHCb detector at centre-of-mass energies of 7,8 and 13 *** data correspond to a total integrated luminosity of 9 fb^-*** significant signal is observed in the mass range from 3.4 to 3.8 GeV/c^*** limits are set at 95%credibility level on the ratio of the ■^+cc production cross-section times the branching fraction to that ofΛ^+c and ■^++cc *** limits are determined as functions of the ■^+cc mass for different lifetime hypotheses,in the rapidity range from 2.0 to 4.5 and the transverse momentum range from 4 to 15 GeV/c.

关键词： charmed baryons limits on production of particles charmed quarks experimental tests

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