Academic literature on the topic 'DEEP framework'
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Journal articles on the topic "DEEP framework"
V, Anjanadevi, Hemalatha R, Venkateshwar R, Naren J, and Vithya G. "A framework for the Diagnosis of Diabetic Retinopathy Using Deep Learning Techniques." International Journal of Psychosocial Rehabilitation 23, no. 1 (February 20, 2019): 405–11. http://dx.doi.org/10.37200/ijpr/v23i1/pr190252.
Full textFiorini, Rodolfo A. "New CICT Framework for Deep Learning and Deep Thinking Application." International Journal of Software Science and Computational Intelligence 8, no. 2 (April 2016): 1–20. http://dx.doi.org/10.4018/ijssci.2016040101.
Full textRichards, Blake A., Timothy P. Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, et al. "A deep learning framework for neuroscience." Nature Neuroscience 22, no. 11 (October 28, 2019): 1761–70. http://dx.doi.org/10.1038/s41593-019-0520-2.
Full textCharalampous, Konstantinos, and Antonios Gasteratos. "A tensor-based deep learning framework." Image and Vision Computing 32, no. 11 (November 2014): 916–29. http://dx.doi.org/10.1016/j.imavis.2014.08.003.
Full textCrunkhorn, Sarah. "Deep learning framework for repurposing drugs." Nature Reviews Drug Discovery 20, no. 2 (January 11, 2021): 100. http://dx.doi.org/10.1038/d41573-021-00006-w.
Full textZhang, Hongjing, Tianyang Zhan, Sugato Basu, and Ian Davidson. "A framework for deep constrained clustering." Data Mining and Knowledge Discovery 35, no. 2 (January 17, 2021): 593–620. http://dx.doi.org/10.1007/s10618-020-00734-4.
Full textSassu, Alessandro, Jose Francisco Saenz-Cogollo, and Maurizio Agelli. "Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams." Sensors 21, no. 12 (June 11, 2021): 4045. http://dx.doi.org/10.3390/s21124045.
Full textYe, Jong Chul, Yoseob Han, and Eunju Cha. "Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems." SIAM Journal on Imaging Sciences 11, no. 2 (January 2018): 991–1048. http://dx.doi.org/10.1137/17m1141771.
Full textXu, Erci, and Shanshan Li. "Revisiting Resource Management for Deep Learning Framework." Electronics 8, no. 3 (March 16, 2019): 327. http://dx.doi.org/10.3390/electronics8030327.
Full textSallab, AhmadEL, Mohammed Abdou, Etienne Perot, and Senthil Yogamani. "Deep Reinforcement Learning framework for Autonomous Driving." Electronic Imaging 2017, no. 19 (January 29, 2017): 70–76. http://dx.doi.org/10.2352/issn.2470-1173.2017.19.avm-023.
Full textDissertations / Theses on the topic "DEEP framework"
Haque, Ashraful. "A Deep Learning-based Dynamic Demand Response Framework." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104927.
Full textDoctor of Philosophy
The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.
Rawat, Sharad. "DEEP LEARNING BASED FRAMEWORK FOR STRUCTURAL TOPOLOGY DESIGN." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1559560543458263.
Full textWaldow, Walter E. "An Adversarial Framework for Deep 3D Target Template Generation." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1597334881614898.
Full textKylén, Jonas. "Deep compositing in VFX : Creating a framework for deciding when to render deep images or traditional renders." Thesis, Luleå tekniska universitet, Institutionen för konst, kommunikation och lärande, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74559.
Full textLavangnananda, Kittichai. "A framework for qualitative model-based reasoning about mechanisms." Thesis, Cardiff University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341744.
Full textHanchate, Narender. "A game theoretic framework for interconnect optimization in deep submicron and nanometer design." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001523.
Full textAnzalone, Evan John. "Agent and model-based simulation framework for deep space navigation analysis and design." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52163.
Full textWagh, Ameya Yatindra. "A Deep 3D Object Pose Estimation Framework for Robots with RGB-D Sensors." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1287.
Full textCherti, Mehdi. "Deep generative neural networks for novelty generation : a foundational framework, metrics and experiments." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS029/document.
Full textIn recent years, significant advances made in deep neural networks enabled the creation of groundbreaking technologies such as self-driving cars and voice-enabled personal assistants. Almost all successes of deep neural networks are about prediction, whereas the initial breakthroughs came from generative models. Today, although we have very powerful deep generative modeling techniques, these techniques are essentially being used for prediction or for generating known objects (i.e., good quality images of known classes): any generated object that is a priori unknown is considered as a failure mode (Salimans et al., 2016) or as spurious (Bengio et al., 2013b). In other words, when prediction seems to be the only possible objective, novelty is seen as an error that researchers have been trying hard to eliminate. This thesis defends the point of view that, instead of trying to eliminate these novelties, we should study them and the generative potential of deep nets to create useful novelty, especially given the economic and societal importance of creating new objects in contemporary societies. The thesis sets out to study novelty generation in relationship with data-driven knowledge models produced by deep generative neural networks. Our first key contribution is the clarification of the importance of representations and their impact on the kind of novelties that can be generated: a key consequence is that a creative agent might need to rerepresent known objects to access various kinds of novelty. We then demonstrate that traditional objective functions of statistical learning theory, such as maximum likelihood, are not necessarily the best theoretical framework for studying novelty generation. We propose several other alternatives at the conceptual level. A second key result is the confirmation that current models, with traditional objective functions, can indeed generate unknown objects. This also shows that even though objectives like maximum likelihood are designed to eliminate novelty, practical implementations do generate novelty. Through a series of experiments, we study the behavior of these models and the novelty they generate. In particular, we propose a new task setup and metrics for selecting good generative models. Finally, the thesis concludes with a series of experiments clarifying the characteristics of models that can exhibit novelty. Experiments show that sparsity, noise level, and restricting the capacity of the net eliminates novelty and that models that are better at recognizing novelty are also good at generating novelty
McClintick, Kyle W. "Training Data Generation Framework For Machine-Learning Based Classifiers." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1276.
Full textBooks on the topic "DEEP framework"
Caterini, Anthony L., and Dong Eui Chang. Deep Neural Networks in a Mathematical Framework. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75304-1.
Full textLynch, Paul E. Towards the development of a national regulatory framework for deep sea mining in the Cook Islands. Cook Islands]: Minister of Mines and Natural Resources, 2011.
Find full textForsyth, D. M. Framework for assessing the susceptibility of management areas to deer impacts. Wellington, N.Z: Dept. of Conservation, 2003.
Find full textCochrane, James R. In word and in deed: Towards a practical theology of social transformation : a framework for reflection and training. Pietermaritzburg, Republic of South Africa: Cluster Publications, 1991.
Find full textChang, Dong Eui, and Anthony L. L. Caterini. Deep Neural Networks in a Mathematical Framework. Springer, 2018.
Find full textLi, Wei. C++ Template Metaprogramming in Practice: A Deep Learning Framework. Auerbach Publishers, Incorporated, 2020.
Find full textLi, Wei. C++ Template Metaprogramming in Practice: A Deep Learning Framework. Auerbach Publishers, Incorporated, 2020.
Find full textLi, Wei. C++ Template Metaprogramming in Practice: A Deep Learning Framework. Auerbach Publishers, Incorporated, 2020.
Find full textLi, Wei. C++ Template Metaprogramming in Practice: A Deep Learning Framework. Auerbach Publishers, Incorporated, 2020.
Find full textNovak, Sandi, and Cara Slattery. Deep Discourse: A Framework for Cultivating Student-Led Discussions. Solution Tree, 2016.
Find full textBook chapters on the topic "DEEP framework"
Leke, Collins Achepsah, and Tshilidzi Marwala. "Deep Learning Framework Analysis." In Studies in Big Data, 147–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01180-2_10.
Full textMuzyka, Kamil. "The Problems with an International Legal Framework for Asteroid Mining." In Deep Space Commodities, 123–40. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90303-3_9.
Full textMori, Tomoko. "The Flipped Classroom: An Instructional Framework for Promotion of Active Learning." In Deep Active Learning, 95–109. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5660-4_6.
Full textQi, Lei, Mohammed Khaleel, Wallapak Tavanapong, Adisak Sukul, and David Peterson. "A Framework for Deep Quantification Learning." In Machine Learning and Knowledge Discovery in Databases, 232–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67658-2_14.
Full textAsadi, Ahmad, and Reza Safabakhsh. "The Encoder-Decoder Framework and Its Applications." In Deep Learning: Concepts and Architectures, 133–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31756-0_5.
Full textGatti, Lauren. "Teacher Education in Deep Focus." In Toward a Framework of Resources for Learning to Teach, 9–31. New York: Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/978-1-137-50145-5_2.
Full textCaterini, Anthony L., and Dong Eui Chang. "Introduction and Motivation." In Deep Neural Networks in a Mathematical Framework, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75304-1_1.
Full textCaterini, Anthony L., and Dong Eui Chang. "Mathematical Preliminaries." In Deep Neural Networks in a Mathematical Framework, 11–22. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75304-1_2.
Full textCaterini, Anthony L., and Dong Eui Chang. "Generic Representation of Neural Networks." In Deep Neural Networks in a Mathematical Framework, 23–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75304-1_3.
Full textCaterini, Anthony L., and Dong Eui Chang. "Specific Network Descriptions." In Deep Neural Networks in a Mathematical Framework, 35–58. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75304-1_4.
Full textConference papers on the topic "DEEP framework"
Kumar, Sameer, Joseph Ratterman, Brian Smith, Charles J. Archer, Gabor Dozsa, Gheorghe Almasi, Philip Heidelberger, et al. "The deep computing messaging framework." In the 22nd annual international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1375527.1375544.
Full textLim, Eun-Ji, and Shin-Young Ahn. "Deep Learning Framework using Scalable Shared Memory Buffer Framework." In 2021 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2021. http://dx.doi.org/10.1109/iceic51217.2021.9369801.
Full textXiang Peisu, Tian Ke, and Huang Qinzhen. "A framework of deep Web crawler." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4604881.
Full textBao, Lei, Yunfei Zheng, Xiaoyan Qin, and Haiqiang Dong. "A General Deep Saliency Enhancement Framework." In 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). IEEE, 2020. http://dx.doi.org/10.1109/icbda49040.2020.9101294.
Full textPhan, Robert, Thoai Man Luu, Rachel Davey, and Girija Chetty. "Deep Learning Based Biomedical NER Framework." In 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628740.
Full textKornilova, Anatasiia, Mikhail Salnikov, Olga Novitskaya, Maria Begicheva, Egor Sevriugov, Kirill Shcherbakov, Valeriya Pronina, and Dmitry V. Dylov. "Deep Learning Framework For Mobile Microscopy." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9434133.
Full textFiorini, Rodolfo A. "Deep learning and deep thinking: New application framework by CICT." In 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2016. http://dx.doi.org/10.1109/icci-cc.2016.7862024.
Full textKrylov, Dmitrii, Remi Tachet des Combes, Romain Laroche, Michael Rosenblum, and Dmitry V. Dylov. "Reinforcement Learning Framework for Deep Brain Stimulation Study." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/394.
Full textBuffing, Maarten. "Framework for evolution in double parton scattering." In XXV International Workshop on Deep-Inelastic Scattering and Related Subjects. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.297.0181.
Full textThorne, Robert, Shaun Bailey, Tom Cridge, Lucian Harland-Lang, Alan Martin, and Ricky Nathvani. "Updates of PDFs using the MMHT framework." In XXVII International Workshop on Deep-Inelastic Scattering and Related Subjects. Trieste, Italy: Sissa Medialab, 2019. http://dx.doi.org/10.22323/1.352.0036.
Full textReports on the topic "DEEP framework"
Perry, Frank Vinton, and Richard E. Kelley. Data to Support Development of Geologic Framework Models for the Deep Borehole Field Test. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1392845.
Full textHerriott, T. M., M. A. Wartes, and P. L. Decker. Deep-water canyons and sequence-stratigraphic framework of the Upper Jurassic Naknek Formation, south-central Alaska. Alaska Division of Geological & Geophysical Surveys, 2017. http://dx.doi.org/10.14509/29707.
Full textNeeraj Gupta. Leveraging Regional Exploration to Develop Geologic Framework for CO2 Storage in Deep Formations in Midwestern United States. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/979447.
Full textYu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.
Full textFullan, 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 textLempert, Robert J., Michelle Miro, and Diogo Prosdocimi. A DMDU Guidebook for Transportation Planning Under a Changing Climate. Edited by Benoit Lefevre and Ernesto Monter Flores. Inter-American Development Bank, February 2021. http://dx.doi.org/10.18235/0003042.
Full textPettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41034.
Full textChapman, Ray, Phu Luong, Sung-Chan Kim, and Earl Hayter. Development of three-dimensional wetting and drying algorithm for the Geophysical Scale Transport Multi-Block Hydrodynamic Sediment and Water Quality Transport Modeling System (GSMB). Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41085.
Full textColomb, Claire, and Tatiana Moreira de Souza. Regulating Short-Term Rentals: Platform-based property rentals in European cities: the policy debates. Property Research Trust, May 2021. http://dx.doi.org/10.52915/kkkd3578.
Full textDeep learning for individual heterogeneity: an automatic inference framework. Cemmap, July 2021. http://dx.doi.org/10.47004/wp.cem.2021.2921.
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