{"id":"https://openalex.org/W2920756108","doi":"https://doi.org/10.1109/acssc.2018.8645128","title":"Optimal Training Channel Statistics for Neural-based Decoders","display_name":"Optimal Training Channel Statistics for Neural-based Decoders","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2920756108","doi":"https://doi.org/10.1109/acssc.2018.8645128","mag":"2920756108"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2018.8645128","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645128","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","issn_l":"1058-6393","issn":["1058-6393","2576-2303"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},"type":"conference-paper","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/A5011355310","display_name":"Meryem Benammar","orcid":"https://orcid.org/0000-0002-4443-9256"},"institutions":[{"id":"https://openalex.org/I193033237","display_name":"Institut Sup\u00e9rieur de l'A\u00e9ronautique et de l'Espace (ISAE-SUPAERO)","ror":"https://ror.org/04gyj6s21","country_code":"FR","type":"education","lineage":["https://openalex.org/I193033237"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Meryem Benammar","raw_affiliation_strings":["Department of Electronics, Optronics, and Signal processing, ISAE-Supa\u00e9ro Toulouse, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics, Optronics, and Signal processing, ISAE-Supa\u00e9ro Toulouse, France","institution_ids":["https://openalex.org/I193033237"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071189599","display_name":"Pablo Piantanida","orcid":"https://orcid.org/0000-0002-8717-2117"},"institutions":[{"id":"https://openalex.org/I102197404","display_name":"Universit\u00e9 Paris-Sud","ror":"https://ror.org/028rypz17","country_code":"FR","type":"education","lineage":["https://openalex.org/I102197404"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA","FR"],"is_corresponding":false,"raw_author_name":"Pablo Piantanida","raw_affiliation_strings":["L2S, CentraleSup\u00e9lec-CNRS-Universit\u00e9 Paris-Sud MILA, Universit\u00e9 de Montr\u00e9al Montr\u00e9al, QC H3T 1N8, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L2S, CentraleSup\u00e9lec-CNRS-Universit\u00e9 Paris-Sud MILA, Universit\u00e9 de Montr\u00e9al Montr\u00e9al, QC H3T 1N8, Canada","institution_ids":["https://openalex.org/I102197404","https://openalex.org/I1294671590","https://openalex.org/I70931966"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2157","last_page":"2161"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9969000220298767,"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"}},"topics":[{"id":"https://openalex.org/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9969000220298767,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12029","display_name":"DNA and Biological Computing","score":0.9901999831199646,"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/computer-science","display_name":"Computer science","score":0.7438122034072876},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5416404008865356},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5070052146911621},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4701687693595886},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.470058798789978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4513199031352997},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3927878141403198},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3443196415901184},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3237096965312958},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2207803726196289},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.15646493434906006},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14731663465499878}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7438122034072876},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5416404008865356},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5070052146911621},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4701687693595886},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.470058798789978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4513199031352997},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3927878141403198},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3443196415901184},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3237096965312958},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2207803726196289},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.15646493434906006},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14731663465499878},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2018.8645128","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645128","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","issn_l":"1058-6393","issn":["1058-6393","2576-2303"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1597391834","https://openalex.org/W2118263817","https://openalex.org/W2131194367","https://openalex.org/W2150498905","https://openalex.org/W2584943905","https://openalex.org/W2963408536","https://openalex.org/W2964070430","https://openalex.org/W2964071712"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3203142394","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2161474341","https://openalex.org/W2495260952"],"abstract_inverted_index":{"This":[0],"work":[1],"investigates":[2],"the":[3,17,27,35,40],"design":[4,18],"of":[5,19,29],"End-to-End":[6],"channel":[7,23,45],"coding":[8,65],"based":[9,22],"on":[10,16,66],"deep":[11],"learning.":[12],"The":[13],"focus":[14],"is":[15],"neural":[20],"networks":[21],"decoders.":[24],"We":[25],"demonstrate":[26],"existence":[28],"an":[30,61],"optimal":[31,55],"training":[32,49],"statistic":[33],"for":[34],"cross-entropy":[36],"loss":[37],"which":[38],"allows":[39],"network":[41],"to":[42,44,53,63],"generalize":[43],"statistics":[46],"unseen":[47],"during":[48],"while":[50],"performing":[51],"close":[52],"their":[54],"decision":[56],"rule.":[57],"Numerical":[58],"results":[59],"illustrate":[60],"application":[62],"Polar":[64],"binary":[67],"input":[68],"memoryless":[69],"channels.":[70]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}