{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:44Z","timestamp":1760245064242,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the U.S. Dept. of Education","award":["P200A150306"],"award-info":[{"award-number":["P200A150306"]}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-1815619,CNS-1931042,IIS- 2008155,IIS-1909879"],"award-info":[{"award-number":["CNS-1815619,CNS-1931042,IIS- 2008155,IIS-1909879"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467252","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:09Z","timestamp":1628748729000},"page":"2017-2025","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks"],"prefix":"10.1145","author":[{"given":"Hang","family":"Yin","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John Boaz","family":"Lee","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangnan","family":"Kong","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Hartvigsen","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sihong","family":"Xie","sequence":"additional","affiliation":[{"name":"Lehigh University, Bethlehem, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_2_1_1","DOI":"10.1109\/BigData.2017.8257939"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_2_1","DOI":"10.1109\/CVPR.2017.781"},{"key":"e_1_3_2_2_3_1","volume-title":"Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio , Nicholas L\u00e9onard , and Aaron Courville . Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 , 2013 . Yoshua Bengio, Nicholas L\u00e9onard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_4_1","DOI":"10.1007\/s11263-014-0788-3"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_5_1","DOI":"10.1109\/IJCNN.2018.8489339"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_6_1","DOI":"10.1109\/IJCNN.2015.7280696"},{"key":"e_1_3_2_2_7_1","first-page":"3581","volume-title":"Advances in neural information processing systems","author":"Gal Yarin","year":"2017","unstructured":"Yarin Gal , Jiri Hron , and Alex Kendall . Concrete dropout . In Advances in neural information processing systems , pages 3581 -- 3590 , 2017 . Yarin Gal, Jiri Hron, and Alex Kendall. Concrete dropout. In Advances in neural information processing systems, pages 3581--3590, 2017."},{"key":"e_1_3_2_2_8_1","volume-title":"Spiking deep residual network. arXiv preprint arXiv:1805.01352","author":"Hu Yangfan","year":"2018","unstructured":"Yangfan Hu , Huajin Tang , Yueming Wang , and Gang Pan . Spiking deep residual network. arXiv preprint arXiv:1805.01352 , 2018 . Yangfan Hu, Huajin Tang, Yueming Wang, and Gang Pan. Spiking deep residual network. arXiv preprint arXiv:1805.01352, 2018."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_9_1","DOI":"10.1109\/JPROC.2015.2444094"},{"key":"e_1_3_2_2_10_1","first-page":"7005","volume-title":"Advances in neural information processing systems","author":"Jin Yingyezhe","year":"2018","unstructured":"Yingyezhe Jin , Wenrui Zhang , and Peng Li . Hybrid macro\/micro level backpropagation for training deep spiking neural networks . In Advances in neural information processing systems , pages 7005 -- 7015 , 2018 . Yingyezhe Jin, Wenrui Zhang, and Peng Li. Hybrid macro\/micro level backpropagation for training deep spiking neural networks. In Advances in neural information processing systems, pages 7005--7015, 2018."},{"key":"e_1_3_2_2_11_1","first-page":"2575","volume-title":"Advances in neural information processing systems","author":"Kingma Durk P","year":"2015","unstructured":"Durk P Kingma , Tim Salimans , and Max Welling . Variational dropout and the local reparameterization trick . In Advances in neural information processing systems , pages 2575 -- 2583 , 2015 . Durk P Kingma, Tim Salimans, and Max Welling. Variational dropout and the local reparameterization trick. In Advances in neural information processing systems, pages 2575--2583, 2015."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_12_1","DOI":"10.3389\/fnins.2016.00508"},{"key":"e_1_3_2_2_13_1","volume-title":"Learning sparse neural networks through l_0 regularization. arXiv preprint arXiv:1712.01312","author":"Louizos Christos","year":"2017","unstructured":"Christos Louizos , Max Welling , and Diederik P Kingma . Learning sparse neural networks through l_0 regularization. arXiv preprint arXiv:1712.01312 , 2017 . Christos Louizos, Max Welling, and Diederik P Kingma. Learning sparse neural networks through l_0 regularization. arXiv preprint arXiv:1712.01312, 2017."},{"key":"e_1_3_2_2_14_1","volume-title":"The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712","author":"Maddison Chris J","year":"2016","unstructured":"Chris J Maddison , Andriy Mnih , and Yee Whye Teh . The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 , 2016 . Chris J Maddison, Andriy Mnih, and Yee Whye Teh. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712, 2016."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_15_1","DOI":"10.1002\/aelm.201900060"},{"key":"e_1_3_2_2_16_1","volume-title":"Bayesian variable selection in linear regression. Journal of the american statistical association, 83(404):1023--1032","author":"Mitchell Toby J","year":"1988","unstructured":"Toby J Mitchell and John J Beauchamp . Bayesian variable selection in linear regression. Journal of the american statistical association, 83(404):1023--1032 , 1988 . Toby J Mitchell and John J Beauchamp. Bayesian variable selection in linear regression. Journal of the american statistical association, 83(404):1023--1032, 1988."},{"key":"e_1_3_2_2_17_1","volume-title":"Real-time classification of complex patterns using spike-based learning in neuromorphic vlsi","author":"Mitra Srinjoy","year":"2008","unstructured":"Srinjoy Mitra , Stefano Fusi , and Giacomo Indiveri . Real-time classification of complex patterns using spike-based learning in neuromorphic vlsi . IEEE transactions on biomedical circuits and systems, 3(1):32--42, 2008 . Srinjoy Mitra, Stefano Fusi, and Giacomo Indiveri. Real-time classification of complex patterns using spike-based learning in neuromorphic vlsi. IEEE transactions on biomedical circuits and systems, 3(1):32--42, 2008."},{"key":"e_1_3_2_2_18_1","volume-title":"Block-sparse recurrent neural networks. arXiv preprint arXiv:1711.02782","author":"Narang Sharan","year":"2017","unstructured":"Sharan Narang , Eric Undersander , and Gregory Diamos . Block-sparse recurrent neural networks. arXiv preprint arXiv:1711.02782 , 2017 . Sharan Narang, Eric Undersander, and Gregory Diamos. Block-sparse recurrent neural networks. arXiv preprint arXiv:1711.02782, 2017."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_19_1","DOI":"10.3389\/fnins.2015.00437"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_20_1","DOI":"10.3389\/fnins.2018.00774"},{"key":"e_1_3_2_2_21_1","first-page":"1412","volume-title":"Advances in neural information processing systems","author":"Shrestha Sumit Bam","year":"2018","unstructured":"Sumit Bam Shrestha and Garrick Orchard . Slayer: Spike layer error reassignment in time . In Advances in neural information processing systems , pages 1412 -- 1421 , 2018 . Sumit Bam Shrestha and Garrick Orchard. Slayer: Spike layer error reassignment in time. In Advances in neural information processing systems, pages 1412--1421, 2018."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_22_1","DOI":"10.3389\/fnins.2017.00350"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_23_1","DOI":"10.1016\/j.neunet.2018.12.002"},{"key":"e_1_3_2_2_24_1","volume-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3--4):229--256","author":"Williams Ronald J","year":"1992","unstructured":"Ronald J Williams . Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3--4):229--256 , 1992 . Ronald J Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3--4):229--256, 1992."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_25_1","DOI":"10.3389\/fnins.2018.00331"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_26_1","DOI":"10.1609\/aaai.v33i01.33011311"}],"event":{"sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"acronym":"KDD '21","name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore"},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467252","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3447548.3467252","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467252","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467252","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:28Z","timestamp":1750191508000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":26,"alternative-id":["10.1145\/3447548.3467252","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467252","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}