Academic literature on the topic 'Model-Based Deep Learning'
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Journal articles on the topic "Model-Based Deep Learning"
Wu, Chong. "A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology." International Journal of Machine Learning and Computing 11, no. 3 (May 2021): 182–87. http://dx.doi.org/10.18178/ijmlc.2021.11.3.1033.
Full textSrinivas, Dr Kalyanapu, and Reddy Dr.B.R.S. "Deep Learning based CNN Optimization Model for MR Braing Image Segmentation." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (November 20, 2019): 213–20. http://dx.doi.org/10.5373/jardcs/v11i11/20193190.
Full textLee, A.-Hyun, Hyeongho Bae, Young-Ky Kim, and Chong-kwon Kim. "Deep Reinforcement Learning based MCS Decision Model." Journal of KIISE 49, no. 8 (August 31, 2022): 663–68. http://dx.doi.org/10.5626/jok.2022.49.8.663.
Full textMohammed, Amal Ahmed Hasan, and Jiazhou Chen. "Cleanup Sketched Drawings: Deep Learning-Based Model." Applied Bionics and Biomechanics 2022 (May 6, 2022): 1–17. http://dx.doi.org/10.1155/2022/2238077.
Full textJing, Jing. "Deep Learning-Based Music Quality Analysis Model." Applied Bionics and Biomechanics 2022 (June 13, 2022): 1–6. http://dx.doi.org/10.1155/2022/6213115.
Full textFang, Lidong, Pei Ge, Lei Zhang, Weinan E. null, and Huan Lei. "DeePN$^2$: A Deep Learning-Based Non-Newtonian Hydrodynamic Model." Journal of Machine Learning 1, no. 1 (June 2022): 114–40. http://dx.doi.org/10.4208/jml.220115.
Full textYuan, Zhen, and Jinfeng Liu. "A Hybrid Deep Learning Model for Trash Classification Based on Deep Trasnsfer Learning." Journal of Electrical and Computer Engineering 2022 (June 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/7608794.
Full textDing, Shifei, Lili Guo, and Yanlu Hou. "Extreme learning machine with kernel model based on deep learning." Neural Computing and Applications 28, no. 8 (January 12, 2016): 1975–84. http://dx.doi.org/10.1007/s00521-015-2170-y.
Full textDai, Xiaofeng, and Weidong Zhu. "Intelligent Financial Auditing Model Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (August 28, 2022): 1–5. http://dx.doi.org/10.1155/2022/8282854.
Full textSun, Chongxin, Bo Chen, Youjun Bu, Surong Zhang, Desheng Zhang, and Bingbing Jiang. "Lightweight Traffic Classification Model Based on Deep Learning." Wireless Communications and Mobile Computing 2022 (October 10, 2022): 1–16. http://dx.doi.org/10.1155/2022/3539919.
Full textDissertations / Theses on the topic "Model-Based Deep Learning"
Matsoukas, Christos. "Model Distillation for Deep-Learning-Based Gaze Estimation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261412.
Full textDen senaste utvecklingen inom djupinlärning har hjälp till att förbättra precisionen hos gaze estimation-modeller till nivåer som inte tidigare varit möjliga. Dock kräver djupinlärningsmetoder oftast både stora mängder beräkningar och minne som därmed begränsar dess användning i inbyggda system med små minnes- och beräkningsresurser. Det här arbetet syftar till att kringgå detta problem genom att öka prediktiv kraft i små nätverk som kan användas i inbyggda system, med hjälp av en modellkomprimeringsmetod som kallas distillation". Under begreppet destillation introducerar vi ytterligare en term till den komprimerade modellens totala optimeringsfunktion som är en avgränsande term mellan en komprimerad modell och en kraftfull modell. Vi visar att destillationsmetoden inför mer än bara brus i den komprimerade modellen. Det vill säga lärarens induktiva bias som hjälper studenten att nå ett bättre optimum tack vare adaptive error deduction. Utöver detta visar vi att MobileNet-familjen uppvisar instabila träningsfaser och vi rapporterar att den destillerade MobileNet25 överträffade sin lärare MobileNet50 något. Dessutom undersöker vi nyligen föreslagna träningsmetoder för att förbättra prediktionen hos små och tunna nätverk och vi konstaterar att extremt tunna arkitekturer är svåra att träna. Slutligen föreslår vi en ny träningsmetod baserad på hint-learning och visar att denna teknik hjälper de tunna MobileNets att stabiliseras under träning och ökar dess prediktiva effektivitet.
Lim, Steven. "Recommending TEE-based Functions Using a Deep Learning Model." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104999.
Full textMaster of Science
Improving the security of software systems has become critically important. A trusted execution environment (TEE) is an emerging technology that can help secure software that uses or stores confidential information. To make use of this technology, developers need to identify which pieces of code handle confidential information and should thus be placed in a TEE. However, this process is costly and laborious because it requires the developers to understand the code well enough to make the appropriate changes in order to incorporate a TEE. This process can become challenging for large software that contains millions of lines of code. To help reduce the cost incurred in the process of identifying which pieces of code should be placed within a TEE, this thesis presents ML-TEE, a recommendation system that uses a deep learning model to help reduce the number of lines of code a developer needs to inspect. Our results show that the recommendation system achieves high accuracy as well as a good balance between precision and recall. In addition, we conducted a pilot study and found that participants from the intervention group who used the output from the recommendation system managed to achieve a higher average accuracy and perform the assigned task faster than the participants in the control group.
Hellström, Terese. "Deep-learning based prediction model for dose distributions in lung cancer patients." Thesis, Stockholms universitet, Fysikum, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-196891.
Full textLi, Mengtong. "An intelligent flood evacuation model based on deep learning of various flood scenarios." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263634.
Full textKarlsson, Axel, and Bohan Zhou. "Model-Based versus Data-Driven Control Design for LEACH-based WSN." Thesis, KTH, Maskinkonstruktion (Inst.), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272197.
Full textI samband med det ökande intresset för att implementera så kallade smart cities, har användningen av utbredda trådlösa sensor nätverk (WSN) blivit ett intresseområde. Bland applikationens största utmaningar, finns det fortfarande förbättringar med avseende på energiförbrukning och servicekvalité. Därmed så inriktar sig detta projekt på att utforska en mängd möjliga lösningar för att förbättra energieffektiviteten för dataaggregation inom WSN. Detta gjordes genom att strategiskt justera positionen av den mottagande basstationen samt paketfrekvensen för varje nod. Dessutom påbyggdes low-energy adaptive clustering hierarchy (LEACH) protokollet med WSN:ets laddningstillstånd. För detta examensarbete definierades ett WSN som ett två dimensionellt plan som innehåller sensor noder och en mobil basstation, d.v.s. en basstation som går att flytta. Efter rigorös analys av klustringsmetoder samt dynamiken av ett WSN, utvecklades två kontrollmetoder som bygger på olika kontrollstrategier. Dessa var en modelbaserad MPC kontroller och en datadriven reinforcement learning kontroller som implementerades för att förbättra energieffektiviteten i WSN. För att testa prestandan på dom två kontrollmetoderna, utvecklades en simulations platform baserat på Python, tillsamans med påbyggnaden av LEACH protokollet. Mängden data skickat per energienhet användes som index för att approximera kontrollprestandan. Simuleringsresultaten visar att den modellbaserade kontrollern kunde öka antalet skickade datapacket med 22% jämfört med när LEACH protokollet användes. Medans den datadrivna kontrollern hade en sämre prestanda jämfört med när enbart LEACH protokollet användes men den visade potential för WSN med en mindre storlek. Påbyggnaden av LEACH protokollet gav ingen tydlig ökning med avseende på energieffektiviteten p.g.a. en mängd avvikande resultat.
Lai, Khai Ping. "A deep learning model for automatic image texture classification: Application to vision-based automatic aircraft landing." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/97992/4/Khai_Ping_Lai_Thesis.pdf.
Full textKeisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.
Full textMa, Xiren. "Deep Learning-Based Vehicle Recognition Schemes for Intelligent Transportation Systems." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42247.
Full textLiu, Rongrong. "Multispectral images-based background subtraction using Codebook and deep learning approaches." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA013.
Full textThis dissertation aims to investigate the multispectral images in moving objects detection via background subtraction, both with classical and deep learning-based methods. As an efficient and representative classical algorithm for background subtraction, the traditional Codebook has first been extended to multispectral case. In order to make the algorithm reliable and robust, a self-adaptive mechanism to select optimal parameters has then been proposed. In this frame, new criteria in the matching process are employed and new techniques to build the background model are designed, including box-based Codebook, dynamic Codebook and fusion strategy. The last attempt is to investigate the potential benefit of using multispectral images via convolutional neural networks. Based on the impressive algorithm FgSegNet_v2, the major contributions of this part lie in two aspects: (1) extracting three channels out of seven in the FluxData FD-1665 multispectral dataset to match the number of input channels of the deep model, and (2) proposing a new convolutional encoder to utilize all the multispectral channels available to further explore the information of multispectral images
Rossi, Alex. "Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Find full textBooks on the topic "Model-Based Deep Learning"
Lattery, Mark J. Deep Learning in Introductory Physics: Exploratory Studies of Model-Based Reasoning. Information Age Publishing, 2016.
Find full textKlingler-Vidra, Robyn. The Venture Capital State. Cornell University Press, 2018. http://dx.doi.org/10.7591/cornell/9781501723377.001.0001.
Full textAnderson, James A. Brain Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0012.
Full textBook chapters on the topic "Model-Based Deep Learning"
Sanghi, Nimish. "Model-Based Algorithms." In Deep Reinforcement Learning with Python, 49–76. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6809-4_3.
Full textZhu, Mei-li, Qing-qing Wang, and Jiang-lin Luo. "Lip-Reading Based on Deep Learning Model." In Transactions on Edutainment XV, 32–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59351-6_4.
Full textTao, Fangjian, Chunjie Cao, and Zhihui Liu. "Webshell Detection Model Based on Deep Learning." In Lecture Notes in Computer Science, 408–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24268-8_38.
Full textSatar, Burak, and Ahmet Emir Dirik. "Deep Learning Based Vehicle Make-Model Classification." In Artificial Neural Networks and Machine Learning – ICANN 2018, 544–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_53.
Full textYu, Shengquan, Jinju Duan, and Jingjing Cui. "Double Helix Deep Learning Model Based on Learning Cell." In Blended Learning: Educational Innovation for Personalized Learning, 22–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21562-0_3.
Full textBiswas, Mainak, and Jasjit S. Suri. "Deep-learning Based Autoencoder Model for Label Distribution Learning." In Communications in Computer and Information Science, 59–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10766-5_5.
Full textJatain, Aman, Khushboo Tripathi, and Shalini Bhaskar Bajaj. "Deep Learning-Based Object Recognition and Detection Model." In Deep Learning in Visual Computing and Signal Processing, 123–43. Boca Raton: Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003277224-6.
Full textHan, Daoqi, Junyao Zhang, Yuhang Zhou, Qing Liu, and Nan Yang. "Intelligent Trader Model Based on Deep Reinforcement Learning." In Web Information Systems and Applications, 15–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30952-7_2.
Full textChen, Song, Junpeng Jiang, Xiaofang Zhang, Jinjin Wu, and Gongzheng Lu. "GAN-Based Planning Model in Deep Reinforcement Learning." In Artificial Neural Networks and Machine Learning – ICANN 2020, 323–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61616-8_26.
Full textZhang, Han. "Radar-Based Activity Recognition with Deep Learning Model." In Lecture Notes in Electrical Engineering, 340–48. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8052-6_42.
Full textConference papers on the topic "Model-Based Deep Learning"
Huang, Zhewei, Shuchang Zhou, and Wen Heng. "Learning to Paint With Model-Based Deep Reinforcement Learning." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00880.
Full textRajat, Priyanka Jaroli, Chaitanya Singla, Vivek Bhardwaj, and Srikanta K. Mohapatra. "Deep Learning Model based Novel Semantic Analysis." In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2022. http://dx.doi.org/10.1109/icacite53722.2022.9823741.
Full textHailong Li, Zhendong Wu, and Jianwu Zhang. "Pedestrian detection based on deep learning model." In 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2016. http://dx.doi.org/10.1109/cisp-bmei.2016.7852818.
Full textShah, Yash, Parth Shah, Mansi Patel, Chinmay Khamkar, and Pratik Kanani. "Deep Learning model-based Multimedia forgery detection." In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2020. http://dx.doi.org/10.1109/i-smac49090.2020.9243530.
Full textLiu, Shunqiang. "Improved model search based on distillation framework." In 2nd International Conference on Computer Vision, Image and Deep Learning, edited by Fengjie Cen and Badrul Hisham bin Ahmad. SPIE, 2021. http://dx.doi.org/10.1117/12.2604789.
Full textLiu, Shunqiang. "Improved model search based on distillation framework." In 2nd International Conference on Computer Vision, Image and Deep Learning, edited by Fengjie Cen and Badrul Hisham bin Ahmad. SPIE, 2021. http://dx.doi.org/10.1117/12.2604789.
Full textJamshidi Avanaki, Nasim, Steven Schmidt, Thilo Michael, Saman Zadtootaghaj, and Sebastian Möller. "Deep-BVQM: A Deep-learning Bitstream-based Video Quality Model." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548374.
Full textCalleja, Pablo, Raúl García-Castro, Guadalupe Aguado-de-Cea, and Asunción Gómez-Pérez. "Role-based model for Named Entity Recognition." In RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning. Incoma Ltd. Shoumen, Bulgaria, 2017. http://dx.doi.org/10.26615/978-954-452-049-6_021.
Full textAlmalki, Ali Jaber, and Pawel Wocjan. "Forecasting Method based upon GRU-based Deep Learning Model." In 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2020. http://dx.doi.org/10.1109/csci51800.2020.00096.
Full textRong, Dazhong, Qinming He, and Jianhai Chen. "Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/306.
Full textReports on the topic "Model-Based Deep Learning"
Fullan, Michael, and Joanne Quinn. How Do Disruptive Innovators Prepare Today's Students to Be Tomorrow's Workforce?: Deep Learning: Transforming Systems to Prepare Tomorrow’s Citizens. Inter-American Development Bank, December 2020. http://dx.doi.org/10.18235/0002959.
Full textA Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning. SAE International, December 2020. http://dx.doi.org/10.4271/2020-01-5154.
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