Relevant bibliographies by topics / DEEP framework

Academic literature on the topic 'DEEP framework'

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Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "DEEP framework"

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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.

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Fiorini, 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.

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To achieve reliable system intelligence outstanding results, current computational system modeling and simulation community has to face and to solve two orders of modeling limitations at least. As a solution, the author proposes an exponential, pre-spatial arithmetic scheme (“all-powerful scheme”) by computational information conservation theory (CICT) to overcome the Information Double-Bind (IDB) problem and to thrive on both deterministic noise (DN) and random noise (RN) to develop powerful cognitive computational framework for deep learning, towards deep thinking applications. In a previous paper the author showed and discussed how this new CICT framework can help us to develop even competitive advanced quantum cognitive computational systems. An operative example is presented. This paper is a relevant contribution towards an effective and convenient “Science 2.0” universal computational framework to develop deeper learning and deep thinking system and application at your fingertips and beyond.
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Richards, 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.

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Charalampous, 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.

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Crunkhorn, 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.

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Zhang, 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.

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Sassu, 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.

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Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.
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Ye, 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.

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Xu, 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.

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The recent adoption of deep learning for diverse applications has required infrastructures to be scaled horizontally and hybrid configured vertically. As a result, efficient resource management for distributed deep learning (DDL) frameworks is becoming increasingly important. However, existing techniques for scaling DDL applications rely on general-purpose resource managers originally designed for data intensive applications. In contrast, DDL applications present unique challenges for resource management as compared to traditional big data frameworks, such as a different master–slave communication paradigm, deeper ML models that are more computationally and network bounded than I/O, the use of heterogeneous resources (e.g., GPUs, TPUs) and the variable memory requirement. In addition, most DDL frameworks require data scientists to manually configure the task placement and resource assignment to execute DDL models. In this paper, we present Dike, an automatic resource management framework that transparently makes scheduling decisions for placement and resource assignment to DDL workers and parameter servers, based on the unique characteristics of the DDL model (number and type of parameters and neural network layers), node heterogeneity (CPU/GPU ratios), and input dataset. We implemented Dike as a resource manager for DDL jobs in Tensorflow on top of Apache Mesos. We show that Dike significantly outperformed both manual and static assignment of resource offers to Tensorflow tasks, and achieved at least 95% of the optimal throughput for different DDL models such as ResNet and Inception.
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Sallab, 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.

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Dissertations / Theses on the topic "DEEP framework"

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Haque, Ashraful. "A Deep Learning-based Dynamic Demand Response Framework." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104927.

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The electric power grid is evolving in terms of generation, transmission and distribution network architecture. On the generation side, distributed energy resources (DER) are participating at a much larger scale. Transmission and distribution networks are transforming to a decentralized architecture from a centralized one. Residential and commercial buildings are now considered as active elements of the electric grid which can participate in grid operation through applications such as the Demand Response (DR). DR is an application through which electric power consumption during the peak demand periods can be curtailed. DR applications ensure an economic and stable operation of the electric grid by eliminating grid stress conditions. In addition to that, DR can be utilized as a mechanism to increase the participation of green electricity in an electric grid. The DR applications, in general, are passive in nature. During the peak demand periods, common practice is to shut down the operation of pre-selected electrical equipment i.e., heating, ventilation and air conditioning (HVAC) and lights to reduce power consumption. This approach, however, is not optimal and does not take into consideration any user preference. Furthermore, this does not provide any information related to demand flexibility beforehand. Under the broad concept of grid modernization, the focus is now on the applications of data analytics in grid operation to ensure an economic, stable and resilient operation of the electric grid. The work presented here utilizes data analytics in DR application that will transform the DR application from a static, look-up-based reactive function to a dynamic, context-aware proactive solution. The dynamic demand response framework presented in this dissertation performs three major functionalities: electrical load forecast, electrical load disaggregation and peak load reduction during DR periods. The building-level electrical load forecasting quantifies required peak load reduction during DR periods. The electrical load disaggregation provides equipment-level power consumption. This will quantify the available building-level demand flexibility. The peak load reduction methodology provides optimal HVAC setpoint and brightness during DR periods to reduce the peak demand of a building. The control scheme takes user preference and context into consideration. A detailed methodology with relevant case studies regarding the design process of the network architecture of a deep learning algorithm for electrical load forecasting and load disaggregation is presented. A case study regarding peak load reduction through HVAC setpoint and brightness adjustment is also presented. To ensure the scalability and interoperability of the proposed framework, a layer-based software architecture to replicate the framework within a cloud environment is demonstrated.
Doctor 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.
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Rawat, Sharad. "DEEP LEARNING BASED FRAMEWORK FOR STRUCTURAL TOPOLOGY DESIGN." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1559560543458263.

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Waldow, 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.

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Kylén, 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.

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Lavangnananda, 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.

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Hanchate, 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.

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Anzalone, 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.

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As the number of spacecraft in simultaneous operation continues to grow, there is an increased dependency on ground-based navigation support. The current baseline system for deep space navigation utilizes Earth-based radiometric tracking, which requires long duration, often global, observations to perform orbit determination and generate a state update. The age, complexity, and high utilization of the assets that make up the Deep Space Network (DSN) pose a risk to spacecraft navigation performance. With increasingly complex mission operations, such as automated asteroid rendezvous or pinpoint planetary landing, the need for high accuracy and autonomous navigation capability is further reinforced. The Network-Based Navigation (NNAV) method developed in this research takes advantage of the growing inter-spacecraft communication network infrastructure to allow for autonomous state measurement. By embedding navigation headers into the data packets transmitted between nodes in the communication network, it is possible to provide an additional source of navigation capability. Simulation results indicate that as NNAV is implemented across the deep space network, the state estimation capability continues to improve, providing an embedded navigation network. To analyze the capabilities of NNAV, an analysis and simulation framework is designed that integrates navigation and communication analysis. Model-Based Systems Engineering (MBSE) and Agent-Based Modeling (ABM) techniques are utilized to foster a modular, expandable, and robust framework. This research has developed the Space Navigation Analysis and Performance Evaluation (SNAPE) framework. This framework allows for design, analysis, and optimization of deep space navigation and communication architectures. SNAPE captures high-level performance requirements and bridges them to specific functional requirements of the analytical implementation. The SNAPE framework is implemented in a representative prototype environment using the Python language and verified using industry standard packages. The capability of SNAPE is validated through a series of example test cases. These analyses focus on the performance of specific state measurements to state estimation performance, and demonstrate the core analytic functionality of the framework. Specific cases analyze the effects of initial error and measurement uncertainty on state estimation performance. The timing and frequency of state measurements are also investigated to show the need for frequent state measurements to minimize navigation errors. The dependence of navigation accuracy on timing stability and accuracy is also demonstrated. These test cases capture the functionality of the tool as well as validate its performance. The SNAPE framework is utilized to capture and analyze NNAV, both conceptually and analytically. Multiple evaluation cases are presented that focus on the Mars Science Laboratory's (MSL) Martian transfer mission phase. These evaluation cases validate NNAV and provide concrete evidence of its operational capability for this particular application. Improvement to onboard state estimation performance and reduced reliance on Earth-based assets is demonstrated through simulation of the MSL spacecraft utilizing NNAV processes and embedded packets within a limited network containing DSN and MRO. From the demonstrated state estimation performance, NNAV is shown to be a capable and viable method of deep space navigation. Through its implementation as a state augmentation method, the concept integrates with traditional measurements and reduces the dependence on Earth-based updates. Future development of this concept focuses on a growing network of assets and spacecraft, which allows for improved operational flexibility and accuracy in spacecraft state estimation capability and a growing solar system-wide navigation network.
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Wagh, 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.

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The task of object detection and pose estimation has widely been done using template matching techniques. However, these algorithms are sensitive to outliers and occlusions, and have high latency due to their iterative nature. Recent research in computer vision and deep learning has shown great improvements in the robustness of these algorithms. However, one of the major drawbacks of these algorithms is that they are specific to the objects. Moreover, the estimation of pose depends significantly on their RGB image features. As these algorithms are trained on meticulously labeled large datasets for object's ground truth pose, it is difficult to re-train these for real-world applications. To overcome this problem, we propose a two-stage pipeline of convolutional neural networks which uses RGB images to localize objects in 2D space and depth images to estimate a 6DoF pose. Thus the pose estimation network learns only the geometric features of the object and is not biased by its color features. We evaluate the performance of this framework on LINEMOD dataset, which is widely used to benchmark object pose estimation frameworks. We found the results to be comparable with the state of the art algorithms using RGB-D images. Secondly, to show the transferability of the proposed pipeline, we implement this on ATLAS robot for a pick and place experiment. As the distribution of images in LINEMOD dataset and the images captured by the MultiSense sensor on ATLAS are different, we generate a synthetic dataset out of very few real-world images captured from the MultiSense sensor. We use this dataset to train just the object detection networks used in the ATLAS Robot experiment.
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Cherti, 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.

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Des avancées significatives sur les réseaux de neurones profonds ont récemment permis le développement de technologies importantes comme les voitures autonomes et les assistants personnels intelligents basés sur la commande vocale. La plupart des succès en apprentissage profond concernent la prédiction, alors que les percées initiales viennent des modèles génératifs. Actuellement, même s'il existe des outils puissants dans la littérature des modèles génératifs basés sur les réseaux profonds, ces techniques sont essentiellement utilisées pour la prédiction ou pour générer des objets connus (i.e., des images de haute qualité qui appartiennent à des classes connues) : un objet généré qui est à priori inconnu est considéré comme une erreur (Salimans et al., 2016) ou comme un objet fallacieux (Bengio et al., 2013b). En d'autres termes, quand la prédiction est considérée comme le seul objectif possible, la nouveauté est vue comme une erreur - que les chercheurs ont essayé d'éliminer au maximum. Cette thèse défends le point de vue que, plutôt que d'éliminer ces nouveautés, on devrait les étudier et étudier le potentiel génératif des réseaux neuronaux pour créer de la nouveauté utile - particulièrement sachant l'importance économique et sociétale de la création d'objets nouveaux dans les sociétés contemporaines. Cette thèse a pour objectif d'étudier la génération de la nouveauté et sa relation avec les modèles de connaissance produits par les réseaux neurones profonds génératifs. Notre première contribution est la démonstration de l'importance des représentations et leur impact sur le type de nouveautés qui peuvent être générées : une conséquence clé est qu'un agent créatif a besoin de re-représenter les objets connus et utiliser cette représentation pour générer des objets nouveaux. Ensuite, on démontre que les fonctions objectives traditionnelles utilisées dans la théorie de l'apprentissage statistique, comme le maximum de vraisemblance, ne sont pas nécessairement les plus adaptées pour étudier la génération de nouveauté. On propose plusieurs alternatives à un niveau conceptuel. Un deuxième résultat clé est la confirmation que les modèles actuels - qui utilisent les fonctions objectives traditionnelles - peuvent en effet générer des objets inconnus. Cela montre que même si les fonctions objectives comme le maximum de vraisemblance s'efforcent à éliminer la nouveauté, les implémentations en pratique échouent à le faire. A travers une série d'expérimentations, on étudie le comportement de ces modèles ainsi que les objets qu'ils génèrent. En particulier, on propose une nouvelle tâche et des métriques pour la sélection de bons modèles génératifs pour la génération de la nouveauté. Finalement, la thèse conclue avec une série d'expérimentations qui clarifie les caractéristiques des modèles qui génèrent de la nouveauté. Les expériences montrent que la sparsité, le niveaux du niveau de corruption et la restriction de la capacité des modèles tuent la nouveauté et que les modèles qui arrivent à reconnaître des objets nouveaux arrivent généralement aussi à générer de la nouveauté
In 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
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McClintick, Kyle W. "Training Data Generation Framework For Machine-Learning Based Classifiers." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1276.

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In this thesis, we propose a new framework for the generation of training data for machine learning techniques used for classification in communications applications. Machine learning-based signal classifiers do not generalize well when training data does not describe the underlying probability distribution of real signals. The simplest way to accomplish statistical similarity between training and testing data is to synthesize training data passed through a permutation of plausible forms of noise. To accomplish this, a framework is proposed that implements arbitrary channel conditions and baseband signals. A dataset generated using the framework is considered, and is shown to be appropriately sized by having $11\%$ lower entropy than state-of-the-art datasets. Furthermore, unsupervised domain adaptation can allow for powerful generalized training via deep feature transforms on unlabeled evaluation-time signals. A novel Deep Reconstruction-Classification Network (DRCN) application is introduced, which attempts to maintain near-peak signal classification accuracy despite dataset bias, or perturbations on testing data unforeseen in training. Together, feature transforms and diverse training data generated from the proposed framework, teaching a range of plausible noise, can train a deep neural net to classify signals well in many real-world scenarios despite unforeseen perturbations.
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Books on the topic "DEEP framework"

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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.

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Lynch, 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.

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Forsyth, D. M. Framework for assessing the susceptibility of management areas to deer impacts. Wellington, N.Z: Dept. of Conservation, 2003.

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Cochrane, 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.

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Chang, Dong Eui, and Anthony L. L. Caterini. Deep Neural Networks in a Mathematical Framework. Springer, 2018.

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Li, Wei. C++ Template Metaprogramming in Practice: A Deep Learning Framework. Auerbach Publishers, Incorporated, 2020.

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Li, Wei. C++ Template Metaprogramming in Practice: A Deep Learning Framework. Auerbach Publishers, Incorporated, 2020.

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Li, Wei. C++ Template Metaprogramming in Practice: A Deep Learning Framework. Auerbach Publishers, Incorporated, 2020.

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Li, Wei. C++ Template Metaprogramming in Practice: A Deep Learning Framework. Auerbach Publishers, Incorporated, 2020.

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Novak, Sandi, and Cara Slattery. Deep Discourse: A Framework for Cultivating Student-Led Discussions. Solution Tree, 2016.

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Book chapters on the topic "DEEP framework"

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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.

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Muzyka, 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.

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Mori, 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.

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Qi, 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.

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Asadi, 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.

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Gatti, 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.

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Caterini, 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.

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Caterini, 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.

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Caterini, 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.

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Caterini, 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.

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Conference papers on the topic "DEEP framework"

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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.

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Lim, 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.

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Xiang 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.

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Bao, 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.

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Phan, 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.

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Kornilova, 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.

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Fiorini, 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.

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Krylov, 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.

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Malfunctioning neurons in the brain sometimes operate synchronously, reportedly causing many neurological diseases, e.g. Parkinson’s. Suppression and control of this collective synchronous activity are therefore of great importance for neuroscience, and can only rely on limited engineering trials due to the need to experiment with live human brains. We present the first Reinforcement Learning (RL) gym framework that emulates this collective behavior of neurons and allows us to find suppression parameters for the environment of synthetic degenerate models of neurons. We successfully suppress synchrony via RL for three pathological signaling regimes, characterize the framework’s stability to noise, and further remove the unwanted oscillations by engaging multiple PPO agents.
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Buffing, 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.

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Thorne, 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.

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Reports on the topic "DEEP framework"

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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.

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Herriott, 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.

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Neeraj 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.

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Yu, 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.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.
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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.

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Disruptive innovators take advantage of unique opportunities. Prior to COVID-19 progress in Latin America and the Caribbean for integrating technology, learning, and system change has been exceedingly slow. In this paper we first offer a general framework for transforming education. The framework focuses on the provision of technology, innovative ideas in learning and well-being, and what we call systemness which are favorable change factors at the local, middle/regional, and policy levels. We then take up the matter of system reform in Latin America and the Caribbean noting problems and potential. Then, we turn to a specific model in system change that we have developed called New Pedagogies for Deep Learning, a model developed in partnerships with groups of schools in ten countries since 2014. The model consists of three main components: 6 Global Competences (character, citizenship, collaboration, communication, creativity, and critical thinking), 4 learning elements (pedagogy, learning partnerships, learning environments, leveraging digital), and three system conditions (school culture, district/regional culture, and system policy). We offer a case study of relative success based on Uruguay with whom we have been working since 2014. Finally, we identify steps and recommendations for next steps in Latin America for taking action on system reform in the next perioda time that we consider critical for taking advantage of the current pandemic disruption. The next few years will be crucial for either attaining positive breakthroughs or slipping backwards into a reinforced status quo.
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Lempert, 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.

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The effects of climate-related natural hazards pose a significant threat to sustainable development in Latin America and the Caribbean (LAC) region and in particular its transportation sector. Risk Management provides an appropriate framework for assessing and mitigating the impacts of climate change and other climate-related natural hazards on transportation systems and choosing actions to enhance their resilience. However, analysts and policymakers involved in transportation planning, policy, and investment face significant challenges in managing the risks triggered by the effects of climate change. Climate change impacts the lifespan of roads, airports, and railroads as they have time horizons that surpass 40 years, thus making it harder (if not impossible) to forecast with confidence all relevant future events that will affect such infrastructure. In addition, the climate has already changed, so the return frequency of storms, for example, and other extreme events may now be different than suggested by the historical record in ways that are not always currently well understood. Implementing Risk Management under conditions of such uncertainty can prove difficult. Decision Making Under Deep Uncertainty (DMDU) enables Risk Management under conditions of Deep Uncertainty, that is when risks cannot confidently be quantified. This guidebook is aligned with the Disaster and Climate Change Risk Assessment Methodology for IDB projects (IDB 2018) and introduces and provides guidance on applying methods for Decision Making Under Deep Uncertainty (DMDU) to transportation planning. It presents the methodological steps that are necessary for the implementation of DMDU methodologies and reviews several such methods, including scenario planning, Adaptive Pathways, and robust decision making (RDM). This review is geared towards supporting the incorporation of DMDU methods into IDBs transportation sector funding and planning processes.
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Pettit, 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.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.
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Chapman, 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.

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The Environmental Laboratory (EL) and the Coastal and Hydraulics Laboratory (CHL) have jointly completed a number of large-scale hydrodynamic, sediment and water quality transport studies. EL and CHL have successfully executed these studies utilizing the Geophysical Scale Transport Modeling System (GSMB). The model framework of GSMB is composed of multiple process models as shown in Figure 1. Figure 1 shows that the United States Army Corps of Engineers (USACE) accepted wave, hydrodynamic, sediment and water quality transport models are directly and indirectly linked within the GSMB framework. The components of GSMB are the two-dimensional (2D) deep-water wave action model (WAM) (Komen et al. 1994, Jensen et al. 2012), data from meteorological model (MET) (e.g., Saha et al. 2010 - http://journals.ametsoc.org/doi/pdf/10.1175/2010BAMS3001.1), shallow water wave models (STWAVE) (Smith et al. 1999), Coastal Modeling System wave (CMS-WAVE) (Lin et al. 2008), the large-scale, unstructured two-dimensional Advanced Circulation (2D ADCIRC) hydrodynamic model (http://www.adcirc.org), and the regional scale models, Curvilinear Hydrodynamics in three dimensions-Multi-Block (CH3D-MB) (Luong and Chapman 2009), which is the multi-block (MB) version of Curvilinear Hydrodynamics in three-dimensions-Waterways Experiments Station (CH3D-WES) (Chapman et al. 1996, Chapman et al. 2009), MB CH3D-SEDZLJ sediment transport model (Hayter et al. 2012), and CE-QUAL Management - ICM water quality model (Bunch et al. 2003, Cerco and Cole 1994). Task 1 of the DOER project, “Modeling Transport in Wetting/Drying and Vegetated Regions,” is to implement and test three-dimensional (3D) wetting and drying (W/D) within GSMB. This technical note describes the methods and results of Task 1. The original W/D routines were restricted to a single vertical layer or depth-averaged simulations. In order to retain the required 3D or multi-layer capability of MB-CH3D, a multi-block version with variable block layers was developed (Chapman and Luong 2009). This approach requires a combination of grid decomposition, MB, and Message Passing Interface (MPI) communication (Snir et al. 1998). The MB single layer W/D has demonstrated itself as an effective tool in hyper-tide environments, such as Cook Inlet, Alaska (Hayter et al. 2012). The code modifications, implementation, and testing of a fully 3D W/D are described in the following sections of this technical note.
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Colomb, 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.

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Short-term rentals mediated by digital platforms have positive and negative impacts that are unevenly distributed among socio-economic groups and places. Detrimental impacts on the housing market and quality of life of long-term residents have been particular contentious in some cities. • In the 12 cities studied in the report (Amsterdam, Barcelona, Berlin, Brussels, Lisbon, London, Madrid, Milan, Paris, Prague, Rome and Vienna), city governments have responded differently to the growth of short-term rentals. • The emerging local regulations of short-term rentals take multiple forms and exhibit various degrees of stringency, ranging from rare cases of laissez-faire to a few cases of partial prohibition or strict quantitative control. Most city governments have sought to find a middle-ground approach that differentiates between the professional rental of whole units and the occasional rental of one’s home/ primary residence. • The regulation of short-term rentals is contentious and highly politicised. Six broad categories of interest groups and non-state actors actively participate in the debates with contrasting positions: advocates of the ‘sharing’ or ‘collaborative’ economy; corporate platforms; professional organisatons of short-term rental operators; new associations of hosts or ‘home-sharers’; the hotel and hospitality industry; and residents’ associations/citizens’ movements. • All city governments face difficulties in implementing and enforcing the regulations, due to a lack of sufficient resources and to the absence of accurate and comprehensive data on individual hosts. That data is held by corporate platforms, which have generally not accepted to release it (with a few exceptions) nor to monitor the content of their listings against local rules. • The relationships between platforms and city governments have oscillated between collaboration and conflict. Effective implementation is impossible without the cooperation of platforms. • In the context of the European Union, the debate has taken a supranational dimension, as two pieces of EU law frame the possibility — and acceptable forms — of regulation of online platforms and of short-term rentals in EU member states: the 2000 E-Commerce Directive and the 2006 Services Directive. • For regulation to be effective, the EU legal framework should be revised to ensure platform account- ability and data disclosure. This would allow city (and other ti ers of) governments to effectively enforce the regulations that they deem appropriate. • Besides, national and regional governments, who often control the legislative framework that defines particular types of short-term rentals, need to give local governments the necessary tools to be able to exercise their ‘right to regulate’ in the name of public interest objectives.
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Deep 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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