{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T08:14:28Z","timestamp":1759565668217,"version":"3.28.0"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1109\/ijcnn48605.2020.9207535","type":"proceedings-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T20:40:33Z","timestamp":1601412033000},"page":"1-8","source":"Crossref","is-referenced-by-count":6,"title":["Improved Polynomial Neural Networks with Normalised Activations"],"prefix":"10.1109","author":[{"given":"Mohit","family":"Goyal","sequence":"first","affiliation":[]},{"given":"Rajan","family":"Goyal","sequence":"additional","affiliation":[]},{"given":"Brejesh","family":"Lall","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"ii1908ii1916","article-title":"Learning polynomials with neural networks","volume":"32","author":"andoni","year":"0"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/35016072"},{"key":"ref12","article-title":"Learning activation functions: A new paradigm of understanding neural net-works","author":"goyal","year":"2019","journal-title":"CoRR"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref14","first-page":"9","article-title":"Efficient backprop","author":"lecun","year":"0"},{"key":"ref15","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"37","author":"ioffe","year":"2015","journal-title":"Proceedings of The 32nd International Conference on Machine Learning"},{"article-title":"Adam: A method for stochastic optimization","year":"0","author":"kingma","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/0095-0696(78)90006-2"},{"key":"ref18","first-page":"192","article-title":"The Loss Surfaces of Multilayer Networks","volume":"38","author":"choromanska","year":"2015","journal-title":"Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics"},{"key":"ref19","article-title":"On the optimization of deep networks: Implicit acceleration by overparameterization","author":"arora","year":"2018","journal-title":"CoRR"},{"key":"ref4","article-title":"On the expressive power of deep polynomial neural networks","author":"kileel","year":"2019","journal-title":"CoRR"},{"key":"ref3","first-page":"855","article-title":"On the computational efficiency of training neural networks","author":"livni","year":"2014","journal-title":"Advances in Neural Information Processing Systems 27"},{"key":"ref6","article-title":"A new type of neurons for machine learning","author":"fan","year":"2017","journal-title":"CoRR"},{"key":"ref5","first-page":"1417","article-title":"Towards provable learning of polynomial neural networks using low-rank matrix estimation","volume":"84","author":"soltani","year":"0"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0893-6080(05)80131-5","article-title":"Multilayer feedforward networks with a nonpolynomial activation function can approximate any function","volume":"6","author":"leshno","year":"1993","journal-title":"Neural Networks"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2916743"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1972.1099931"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04772-5_77"},{"key":"ref9","article-title":"Universal approximation with quadratic deep networks","author":"fan","year":"2018","journal-title":"CoRR"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/18.256500"},{"key":"ref22","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref21","first-page":"ii?1908","article-title":"Learning polynomials with neural networks","author":"andoni","year":"0"},{"article-title":"Deep residual learning for image recognition","year":"2015","author":"he","key":"ref24"},{"key":"ref23","article-title":"Saga: A fast incremental gradient method with support for non-strongly convex composite objectives","volume":"2","author":"defazio","year":"2014","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2020 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2020,7,19]]},"location":"Glasgow, United Kingdom","end":{"date-parts":[[2020,7,24]]}},"container-title":["2020 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9200848\/9206590\/09207535.pdf?arnumber=9207535","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T17:57:15Z","timestamp":1656439035000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9207535\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/ijcnn48605.2020.9207535","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}