{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:27:51Z","timestamp":1775665671193,"version":"3.50.1"},"reference-count":49,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000893","name":"Simons Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000893","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006754","name":"Army Research Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006754","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,15]]},"DOI":"10.1109\/bigdata52589.2021.9671368","type":"proceedings-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T20:39:16Z","timestamp":1642106356000},"page":"3865-3870","source":"Crossref","is-referenced-by-count":9,"title":["Activation Landscapes as a Topological Summary of Neural Network Performance"],"prefix":"10.1109","author":[{"given":"Matthew","family":"Wheeler","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose","family":"Bouza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Bubenik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"ref38","article-title":"Local homology of abstract simplicial complexes","author":"robinson","year":"2018","journal-title":"ArXiv"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-019-09424-0"},{"key":"ref32","article-title":"Perseus: the persistent homology software","author":"nanda","year":"2013"},{"key":"ref31","first-page":"40","article-title":"Topology of deep neural networks","volume":"21","author":"naitzat","year":"2020","journal-title":"J Mach Learn Res"},{"key":"ref30","article-title":"Dionysus: a C++ library with various algorithms for computing persistent homology","author":"morozov","year":"2012"},{"key":"ref37","article-title":"Neural persistence: A complexity measure for deep neural networks using algebraic topology","author":"rieck","year":"2019","journal-title":"International Conference on Learning Representations"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14195"},{"key":"ref35","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"paszke","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1115\/DETC2020-22675"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2016.00094"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1021\/ci400187y"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s41468-020-00062-y"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.114.111801"},{"key":"ref1","first-page":"465534","article-title":"The DeepTune framework for modeling and characterizing neurons in visual cortex area V4","author":"abbasi-asl","year":"2018","journal-title":"BioRxiv"},{"key":"ref20","article-title":"Deep speech: Scaling up end-to-end speech recognition","author":"hannun","year":"2014","journal-title":"ArXiv"},{"key":"ref22","first-page":"4868","article-title":"Connectivity-optimized representation learning via persistent homology","volume":"2019 june","author":"hofer","year":"2019","journal-title":"36th International Conference on Machine Learning ICML 2019"},{"key":"ref21","first-page":"1634","article-title":"Deep learning with topological signatures","author":"hofer","year":"2017","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref24","first-page":"15965","article-title":"Pllay: Efficient topological layer based on persistent landscapes","volume":"33","author":"kim","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2843369"},{"key":"ref26","author":"liu","year":"2016","journal-title":"Applying topological persistence in convolutional neural network for music audio signals"},{"key":"ref25","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"3rd International Conference on Learning Representations ICLR 2015 San Diego CA USA May 7-9 2015 Conference Track Proceedings"},{"key":"ref10","first-page":"2786","article-title":"Perslay: A neural network layer for persistence diagrams and new graph topological signatures","author":"carri\u00e8re","year":"2020","journal-title":"The 23rd International Conference on Artificial Intelligence and Statistics AISTATS 2020"},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.23915\/distill.00015","article-title":"Activation atlas","author":"carter","year":"2019","journal-title":"Distillation"},{"key":"ref40","article-title":"Topological methods for the analysis of high dimensional data sets and 3d object recognition","volume":"22","author":"singh","year":"2007","journal-title":"Eurographics Symposium on Point-Based Graphics"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0107-6"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts475"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47358-7_17"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s00454-002-2885-2"},{"key":"ref16","first-page":"1553","article-title":"A topology layer for machine learning","author":"gabrielsson","year":"2020","journal-title":"volume 108 of Proceedings of Machine Learning Research"},{"key":"ref17","article-title":"Deep learning","author":"goodfellow","year":"2016","journal-title":"Adaptive Computation and Machine Learning"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref19","article-title":"On characterizing the capacity of neural networks using algebraic topology","author":"guss","year":"2018","journal-title":"ArXiv"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s41468-021-00071-5"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2021.681174"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973099.107"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44199-2_24"},{"key":"ref8","first-page":"77","article-title":"Statistical topological data analysis using persistence landscapes","volume":"16","author":"bubenik","year":"2015","journal-title":"Journal of Machine Learning Research"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00180"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/s00454-004-1146-y"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-43408-3_5"},{"key":"ref46","first-page":"4","article-title":"Conversational speech transcription using context-dependent deep neural networks","author":"yu","year":"2012","journal-title":"Proceedings of the 29th International Conference on Machine Learning ICML 2012"},{"key":"ref45","article-title":"Understanding neural networks through deep visualization","author":"yosinski","year":"2015"},{"key":"ref48","article-title":"Evaluating the disentanglement of deep generative models through manifold topology","author":"zhou","year":"2021"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1527\/tjsai.D-G72"},{"key":"ref41","article-title":"giottotda: a topological data analysis toolkit for machine learning and data exploration","volume":"22","author":"tauzin","year":"2021","journal-title":"J Mach Learn Res"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-021-09761-3"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_8"}],"event":{"name":"2021 IEEE International Conference on Big Data (Big Data)","location":"Orlando, FL, USA","start":{"date-parts":[[2021,12,15]]},"end":{"date-parts":[[2021,12,18]]}},"container-title":["2021 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9671263\/9671273\/09671368.pdf?arnumber=9671368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:55:29Z","timestamp":1652201729000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9671368\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/bigdata52589.2021.9671368","relation":{},"subject":[],"published":{"date-parts":[[2021,12,15]]}}}