Praveen Thachappully Adithya, R. Muthalagu, Sapna Sadhwani
{"title":"创世纪网:使用强化学习的微调密集网络配置","authors":"Praveen Thachappully Adithya, R. Muthalagu, Sapna Sadhwani","doi":"10.1145/3439133.3439139","DOIUrl":null,"url":null,"abstract":"Designing neural networks even in the case of relatively simpler fully connected neural networks / dense networks is a time-consuming process since the architecture design is done manually based on intuition and manual tweaking. In this paper, we present “Genesis Net”, a dense net that starts off with a very basic configuration (“seed configuration”), and subsequently tweaks itself via reinforcement learning (RL) to arrive at an optimal configuration for the task at hand. Genesis Net attained a test error within 0.59% of a similar but bigger documented baseline model. Furthermore, our model was able to achieve this using merely 10.11% of trainable weights that the baseline model used. This significantly smaller network was found using Q-Learning combined with a dynamic action space that allowed for fine tuning the network configuration.","PeriodicalId":291985,"journal":{"name":"2020 4th International Conference on Artificial Intelligence and Virtual Reality","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Genesis Net: Fine Tuned Dense Net Configuration using Reinforcement Learning\",\"authors\":\"Praveen Thachappully Adithya, R. Muthalagu, Sapna Sadhwani\",\"doi\":\"10.1145/3439133.3439139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Designing neural networks even in the case of relatively simpler fully connected neural networks / dense networks is a time-consuming process since the architecture design is done manually based on intuition and manual tweaking. In this paper, we present “Genesis Net”, a dense net that starts off with a very basic configuration (“seed configuration”), and subsequently tweaks itself via reinforcement learning (RL) to arrive at an optimal configuration for the task at hand. Genesis Net attained a test error within 0.59% of a similar but bigger documented baseline model. Furthermore, our model was able to achieve this using merely 10.11% of trainable weights that the baseline model used. This significantly smaller network was found using Q-Learning combined with a dynamic action space that allowed for fine tuning the network configuration.\",\"PeriodicalId\":291985,\"journal\":{\"name\":\"2020 4th International Conference on Artificial Intelligence and Virtual Reality\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 4th International Conference on Artificial Intelligence and Virtual Reality\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3439133.3439139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 4th International Conference on Artificial Intelligence and Virtual Reality","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3439133.3439139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genesis Net: Fine Tuned Dense Net Configuration using Reinforcement Learning
Designing neural networks even in the case of relatively simpler fully connected neural networks / dense networks is a time-consuming process since the architecture design is done manually based on intuition and manual tweaking. In this paper, we present “Genesis Net”, a dense net that starts off with a very basic configuration (“seed configuration”), and subsequently tweaks itself via reinforcement learning (RL) to arrive at an optimal configuration for the task at hand. Genesis Net attained a test error within 0.59% of a similar but bigger documented baseline model. Furthermore, our model was able to achieve this using merely 10.11% of trainable weights that the baseline model used. This significantly smaller network was found using Q-Learning combined with a dynamic action space that allowed for fine tuning the network configuration.