{"id":"https://openalex.org/W4387870558","doi":"https://doi.org/10.1109/mlsp55844.2023.10285981","title":"Amortized Variational Peak Fitting For Spectroscopic Data","display_name":"Amortized Variational Peak Fitting For Spectroscopic Data","publication_year":2023,"publication_date":"2023-09-17","ids":{"openalex":"https://openalex.org/W4387870558","doi":"https://doi.org/10.1109/mlsp55844.2023.10285981"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp55844.2023.10285981","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/mlsp55844.2023.10285981","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046525024","display_name":"David Frich Hansen","orcid":"https://orcid.org/0000-0001-6135-5823"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"David Frich Hansen","raw_affiliation_strings":["Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby,Dept. of Applied Mathematics and Computer Science,Denmark","Dept. of Applied Mathematics and Computer Science, Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby, Denmark"],"affiliations":[{"raw_affiliation_string":"Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby,Dept. of Applied Mathematics and Computer Science,Denmark","institution_ids":["https://openalex.org/I96673099"]},{"raw_affiliation_string":"Dept. of Applied Mathematics and Computer Science, Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064146030","display_name":"Tommy Sonne Alstr\u00f8m","orcid":"https://orcid.org/0000-0003-0941-3146"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Tommy Sonne Alstr\u00f8m","raw_affiliation_strings":["Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby,Dept. of Applied Mathematics and Computer Science,Denmark","Dept. of Applied Mathematics and Computer Science, Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby, Denmark"],"affiliations":[{"raw_affiliation_string":"Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby,Dept. of Applied Mathematics and Computer Science,Denmark","institution_ids":["https://openalex.org/I96673099"]},{"raw_affiliation_string":"Dept. of Applied Mathematics and Computer Science, Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073902328","display_name":"Mikkel N. Schmidt","orcid":"https://orcid.org/0000-0001-6927-8869"},"institutions":[{"id":"https://openalex.org/I96673099","display_name":"Technical University of Denmark","ror":"https://ror.org/04qtj9h94","country_code":"DK","type":"education","lineage":["https://openalex.org/I96673099"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Mikkel N. Schmidt","raw_affiliation_strings":["Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby,Dept. of Applied Mathematics and Computer Science,Denmark","Dept. of Applied Mathematics and Computer Science, Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby, Denmark"],"affiliations":[{"raw_affiliation_string":"Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby,Dept. of Applied Mathematics and Computer Science,Denmark","institution_ids":["https://openalex.org/I96673099"]},{"raw_affiliation_string":"Dept. of Applied Mathematics and Computer Science, Technical University of Denmark Richard Petersens Plads 324, 2800 Kgs. Lyngby, Denmark","institution_ids":["https://openalex.org/I96673099"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046525024"],"corresponding_institution_ids":["https://openalex.org/I96673099"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14094795,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5847163200378418},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5557392239570618},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5525208711624146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.551497757434845},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5342807769775391},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5208576321601868},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5207417011260986},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4997565746307373},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.4612810015678406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3551028370857239},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3235433101654053},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14517110586166382}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5847163200378418},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5557392239570618},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5525208711624146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.551497757434845},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5342807769775391},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5208576321601868},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5207417011260986},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4997565746307373},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.4612810015678406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3551028370857239},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3235433101654053},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14517110586166382},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mlsp55844.2023.10285981","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/mlsp55844.2023.10285981","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/66df2837-b61a-4eef-96f3-592e3f461169","is_oa":false,"landing_page_url":"https://orbit.dtu.dk/en/publications/66df2837-b61a-4eef-96f3-592e3f461169","pdf_url":null,"source":{"id":"https://openalex.org/S4306400705","display_name":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I96673099","host_organization_name":"Technical University of Denmark","host_organization_lineage":["https://openalex.org/I96673099"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Hansen , D F , Alstrom , T S & Schmidt , M N 2023 , Amortized Variational Peak Fitting For Spectroscopic Data . in Proceedings of IEEE 33 rd International Workshop on Machine Learning for Signal Processing . IEEE , 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing , Rome , Italy , 17/09/2023 . https://doi.org/10.1109/MLSP55844.2023.10285981","raw_type":"bookPart"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W1986157931","https://openalex.org/W2596765096","https://openalex.org/W2950151997","https://openalex.org/W3178565336","https://openalex.org/W4226081797","https://openalex.org/W4254257835","https://openalex.org/W4287123203","https://openalex.org/W4288310870","https://openalex.org/W4294562888","https://openalex.org/W4386295575","https://openalex.org/W6640963894","https://openalex.org/W6684578138","https://openalex.org/W6729448088","https://openalex.org/W6765826203","https://openalex.org/W6796203516","https://openalex.org/W6811522774"],"related_works":["https://openalex.org/W4283077537","https://openalex.org/W2999603699","https://openalex.org/W2464065341","https://openalex.org/W2947536360","https://openalex.org/W2968689489","https://openalex.org/W4302573481","https://openalex.org/W2505308168","https://openalex.org/W2963960970","https://openalex.org/W2407375987","https://openalex.org/W3049691116"],"abstract_inverted_index":{"Spectroscopic":[0],"analysis":[1,108],"relies":[2],"on":[3],"identifying":[4],"and":[5,53,91,102,120],"understanding":[6],"the":[7,49,79],"spectral":[8,107,114],"peaks":[9,52],"that":[10],"represent":[11],"unique":[12],"characteristics":[13],"of":[14,51,81],"an":[15,39],"analyte.":[16],"In":[17,70],"high-speed":[18,118],"real-time":[19,113],"settings,":[20],"current":[21],"peak":[22,68],"fitting":[23],"techniques,":[24],"particularly":[25],"Bayesian":[26,106],"methods":[27],"involving":[28],"MCMC":[29,97],"or":[30],"variational":[31,62,89],"approximation,":[32],"can":[33],"be":[34],"prohibitively":[35],"expensive.":[36],"We":[37],"propose":[38],"unsupervised":[40],"method":[41,76,104],"using":[42],"a":[43,65,71],"convolutional":[44],"neural":[45],"network":[46],"to":[47,87,96],"estimate":[48],"number":[50,80],"their":[54],"parameters":[55,85],"along":[56],"with":[57],"posterior":[58],"uncertainty,":[59],"by":[60],"amortizing":[61],"inference":[63],"in":[64,112],"classical":[66],"parametric":[67],"model.":[69],"simulated":[72],"data":[73,115],"study,":[74],"our":[75],"reliably":[77],"determines":[78],"peaks,":[82],"precisely":[83],"estimates":[84],"similar":[86],"direct":[88],"inference,":[90],"accurately":[92],"captures":[93],"uncertainty":[94],"comparable":[95],"methods.":[98],"Our":[99],"novel,":[100],"fast,":[101],"precise":[103],"for":[105,117],"opens":[109],"new":[110],"possibilities":[111],"processing":[116],"monitoring":[119],"control.":[121]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}