{"id":"https://openalex.org/W3025590998","doi":"https://doi.org/10.1109/wacv45572.2020.9093542","title":"Adaptive Neural Connections for Sparsity Learning","display_name":"Adaptive Neural Connections for Sparsity Learning","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3025590998","doi":"https://doi.org/10.1109/wacv45572.2020.9093542","mag":"3025590998"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093542","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093542","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5004428172","display_name":"Alex Gain","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alex Gain","raw_affiliation_strings":["Department of Computer Science, Johns Hopkins University, Baltimore, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Johns Hopkins University, Baltimore, MD","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025289100","display_name":"Prakhar Kaushik","orcid":"https://orcid.org/0000-0001-6449-8088"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prakhar Kaushik","raw_affiliation_strings":["Department of Computer Science, Johns Hopkins University, Baltimore, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Johns Hopkins University, Baltimore, MD","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038789440","display_name":"Hava T. Siegelmann","orcid":"https://orcid.org/0000-0003-4938-8723"},"institutions":[{"id":"https://openalex.org/I177605424","display_name":"Amherst College","ror":"https://ror.org/028vqfs63","country_code":"US","type":"education","lineage":["https://openalex.org/I177605424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hava Siegelmann","raw_affiliation_strings":["School of Computer Science, University of Massachussetts Amherst, Amherst, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Massachussetts Amherst, Amherst, MA","institution_ids":["https://openalex.org/I177605424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3177","last_page":"3182"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7564195990562439},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5956347584724426},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5914819836616516},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5737968683242798},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5679429173469543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.562109112739563},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5292932987213135},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.45779094099998474},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.42521944642066956},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4223852753639221},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.42140138149261475},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4102594256401062},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3715440630912781},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3398282527923584},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3210996091365814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7564195990562439},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5956347584724426},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5914819836616516},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5737968683242798},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5679429173469543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.562109112739563},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5292932987213135},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.45779094099998474},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.42521944642066956},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4223852753639221},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.42140138149261475},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4102594256401062},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3715440630912781},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3398282527923584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3210996091365814},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093542","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093542","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1567512734","https://openalex.org/W1826234144","https://openalex.org/W2171980229","https://openalex.org/W2741430497","https://openalex.org/W2764043458","https://openalex.org/W2895171208","https://openalex.org/W2896409484","https://openalex.org/W2898870584","https://openalex.org/W2911546748","https://openalex.org/W2915589364","https://openalex.org/W2951569836","https://openalex.org/W2951595529","https://openalex.org/W2959071218","https://openalex.org/W2962851801","https://openalex.org/W2963000224","https://openalex.org/W2963828549","https://openalex.org/W4394639701","https://openalex.org/W6638836233","https://openalex.org/W6684942582","https://openalex.org/W6725543821","https://openalex.org/W6741978826","https://openalex.org/W6745148473","https://openalex.org/W6745499552","https://openalex.org/W6755174528","https://openalex.org/W6755821940","https://openalex.org/W6755843862","https://openalex.org/W6759263581","https://openalex.org/W6765719518"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W4239286941","https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1966421350","https://openalex.org/W2782041652","https://openalex.org/W1868434454","https://openalex.org/W4366985237","https://openalex.org/W2612657834","https://openalex.org/W2392157706"],"abstract_inverted_index":{"Sparsity":[0,91],"learning":[1,33],"aims":[2],"to":[3,128],"decrease":[4],"the":[5,101,114],"computational":[6],"and":[7,46,104],"memory":[8],"costs":[9],"of":[10,28],"large":[11,26],"deep":[12],"neural":[13,18],"networks":[14],"(DNNs)":[15],"via":[16,71,97],"pruning":[17,45],"connections":[19,70,85],"while":[20],"simultaneously":[21],"retaining":[22],"high":[23],"accuracy.":[24],"A":[25],"body":[27],"work":[29],"has":[30],"developed":[31],"sparsity":[32],"approaches,":[34],"with":[35,108,134,140],"recent":[36],"large-scale":[37],"experiments":[38],"showing":[39],"that":[40,77,137],"two":[41,87],"main":[42],"methods,":[43],"magnitude":[44],"Variational":[47],"Dropout":[48],"(VD),":[49],"achieve":[50],"similar":[51],"state-of-the-art":[52],"results":[53],"for":[54,65,96,131],"classification":[55],"tasks.":[56],"We":[57],"propose":[58],"Adaptive":[59],"Neural":[60],"Connections":[61],"(ANC),":[62],"a":[63],"method":[64],"explicitly":[66,94],"parameterizing":[67,83],"fine-grained":[68],"neuron-to-neuron":[69,84],"adjacency":[72,102],"matrices":[73],"at":[74],"each":[75],"layer":[76],"are":[78],"learned":[79,120],"through":[80],"backpropagation.":[81],"Explicitly":[82],"confers":[86],"primary":[88],"advantages:":[89],"1.":[90],"can":[92,116],"be":[93,117],"optimized":[95],"norm-based":[98],"regularization":[99],"on":[100],"matrices;":[103],"2.":[105],"When":[106],"combined":[107],"VD":[109],"(which":[110],"we":[111,125],"term,":[112],"ANC-VD),":[113],"adjacencies":[115],"interpreted":[118],"as":[119],"weight":[121],"importance":[122],"parameters,":[123],"which":[124],"hypothesize":[126],"leads":[127],"improved":[129],"convergence":[130],"VD.":[132],"Experiments":[133],"ResNet18":[135],"show":[136],"architectures":[138],"augmented":[139],"ANC":[141],"outperform":[142],"their":[143],"vanilla":[144],"counterparts.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
