{"title":"深度学习(部分)揭开神秘面纱","authors":"V. Kreinovich, O. Kosheleva","doi":"10.1145/3396474.3396481","DOIUrl":null,"url":null,"abstract":"Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices - and the surprising success of deep learning in the first place - can be explained by reasonably simple and natural mathematics.","PeriodicalId":408084,"journal":{"name":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Deep Learning (Partly) Demystified\",\"authors\":\"V. Kreinovich, O. Kosheleva\",\"doi\":\"10.1145/3396474.3396481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices - and the surprising success of deep learning in the first place - can be explained by reasonably simple and natural mathematics.\",\"PeriodicalId\":408084,\"journal\":{\"name\":\"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3396474.3396481\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3396474.3396481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices - and the surprising success of deep learning in the first place - can be explained by reasonably simple and natural mathematics.
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