Literatura académica sobre el tema "Deep learning architecture"
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Artículos de revistas sobre el tema "Deep learning architecture"
Munir, Khushboo, Fabrizio Frezza y Antonello Rizzi. "Deep Learning Hybrid Techniques for Brain Tumor Segmentation". Sensors 22, n.º 21 (26 de octubre de 2022): 8201. http://dx.doi.org/10.3390/s22218201.
Texto completoAlvarez-Gonzalez, Ruben y Andres Mendez-Vazquez. "Deep Learning Architecture Reduction for fMRI Data". Brain Sciences 12, n.º 2 (8 de febrero de 2022): 235. http://dx.doi.org/10.3390/brainsci12020235.
Texto completoKumar, Bhavesh Shri, Naren J, Vithya G y Prahathish K. "A Novel Architecture based on Deep Learning for Scene Image Recognition". International Journal of Psychosocial Rehabilitation 23, n.º 1 (20 de febrero de 2019): 400–404. http://dx.doi.org/10.37200/ijpr/v23i1/pr190251.
Texto completoHyunhee Park, Hyunhee Park. "Edge Based Lightweight Authentication Architecture Using Deep Learning for Vehicular Networks". 網際網路技術學刊 23, n.º 1 (enero de 2022): 195–202. http://dx.doi.org/10.53106/160792642022012301020.
Texto completoHao, Xing, Guigang Zhang y Shang Ma. "Deep Learning". International Journal of Semantic Computing 10, n.º 03 (septiembre de 2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.
Texto completoZou, Han, Jing Ge, Ruichao Liu y Lin He. "Feature Recognition of Regional Architecture Forms Based on Machine Learning: A Case Study of Architecture Heritage in Hubei Province, China". Sustainability 15, n.º 4 (14 de febrero de 2023): 3504. http://dx.doi.org/10.3390/su15043504.
Texto completoMa, Rui, Jia-Ching Hsu, Tian Tan, Eriko Nurvitadhi, David Sheffield, Rob Pelt, Martin Langhammer, Jaewoong Sim, Aravind Dasu y Derek Chiou. "Specializing FGPU for Persistent Deep Learning". ACM Transactions on Reconfigurable Technology and Systems 14, n.º 2 (8 de julio de 2021): 1–23. http://dx.doi.org/10.1145/3457886.
Texto completoSewak, Mohit, Sanjay K. Sahay y Hemant Rathore. "An Overview of Deep Learning Architecture of Deep Neural Networks and Autoencoders". Journal of Computational and Theoretical Nanoscience 17, n.º 1 (1 de enero de 2020): 182–88. http://dx.doi.org/10.1166/jctn.2020.8648.
Texto completoHartanto, Cahyo Adhi y Laksmita Rahadianti. "Single Image Dehazing Using Deep Learning". JOIV : International Journal on Informatics Visualization 5, n.º 1 (22 de marzo de 2021): 76. http://dx.doi.org/10.30630/joiv.5.1.431.
Texto completoGhimire, Deepak, Dayoung Kil y Seong-heum Kim. "A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration". Electronics 11, n.º 6 (18 de marzo de 2022): 945. http://dx.doi.org/10.3390/electronics11060945.
Texto completoTesis sobre el tema "Deep learning architecture"
Glatt, Ruben [UNESP]. "Deep learning architecture for gesture recognition". Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/115718.
Texto completoO reconhecimento de atividade de visão de computador desempenha um papel importante na investigação para aplicações como interfaces humanas de computador, ambientes inteligentes, vigilância ou sistemas médicos. Neste trabalho, é proposto um sistema de reconhecimento de gestos com base em uma arquitetura de aprendizagem profunda. Ele é usado para analisar o desempenho quando treinado com os dados de entrada multi-modais em um conjunto de dados de linguagem de sinais italiana. A área de pesquisa subjacente é um campo chamado interação homem-máquina. Ele combina a pesquisa sobre interfaces naturais, reconhecimento de gestos e de atividade, aprendizagem de máquina e tecnologias de sensores que são usados para capturar a entrada do meio ambiente para processamento posterior. Essas áreas são introduzidas e os conceitos básicos são descritos. O ambiente de desenvolvimento para o pré-processamento de dados e algoritmos de aprendizagem de máquina programada em Python é descrito e as principais bibliotecas são discutidas. A coleta dos fluxos de dados é explicada e é descrito o conjunto de dados utilizado. A arquitetura proposta de aprendizagem consiste em dois passos. O pré-processamento dos dados de entrada e a arquitetura de aprendizagem. O pré-processamento é limitado a três estratégias diferentes, que são combinadas para oferecer seis diferentes perfis de préprocessamento. No segundo passo, um Deep Belief Network é introduzido e os seus componentes são explicados. Com esta definição, 294 experimentos são realizados com diferentes configurações. As variáveis que são alteradas são as definições de pré-processamento, a estrutura de camadas do modelo, a taxa de aprendizagem de pré-treino e a taxa de aprendizagem de afinação. A avaliação dessas experiências mostra que a abordagem de utilização de uma arquitetura ... (Resumo completo, clicar acesso eletrônico abaixo)
Activity recognition from computer vision plays an important role in research towards applications like human computer interfaces, intelligent environments, surveillance or medical systems. In this work, a gesture recognition system based on a deep learning architecture is proposed. It is used to analyze the performance when trained with multi-modal input data on an Italian sign language dataset. The underlying research area is a field called human-machine interaction. It combines research on natural user interfaces, gesture and activity recognition, machine learning and sensor technologies, which are used to capture the environmental input for further processing. Those areas are introduced and the basic concepts are described. The development environment for preprocessing data and programming machine learning algorithms with Python is described and the main libraries are discussed. The gathering of the multi-modal data streams is explained and the used dataset is outlined. The proposed learning architecture consists of two steps. The preprocessing of the input data and the actual learning architecture. The preprocessing is limited to three different strategies, which are combined to offer six different preprocessing profiles. In the second step, a Deep Belief network is introduced and its components are explained. With this setup, 294 experiments are conducted with varying configuration settings. The variables that are altered are the preprocessing settings, the layer structure of the model, the pretraining and the fine-tune learning rate. The evaluation of these experiments show that the approach of using a deep learning architecture on an activity or gesture recognition task yields acceptable results, but has not yet reached a level of maturity, which would allow to use the developed models in serious applications.
Glatt, Ruben. "Deep learning architecture for gesture recognition /". Guaratinguetá, 2014. http://hdl.handle.net/11449/115718.
Texto completoCoorientador: Daniel Julien Barros da Silva Sampaio
Banca: Galeno José de Sena
Banca: Luiz de Siqueira Martins Filho
Resumo: O reconhecimento de atividade de visão de computador desempenha um papel importante na investigação para aplicações como interfaces humanas de computador, ambientes inteligentes, vigilância ou sistemas médicos. Neste trabalho, é proposto um sistema de reconhecimento de gestos com base em uma arquitetura de aprendizagem profunda. Ele é usado para analisar o desempenho quando treinado com os dados de entrada multi-modais em um conjunto de dados de linguagem de sinais italiana. A área de pesquisa subjacente é um campo chamado interação homem-máquina. Ele combina a pesquisa sobre interfaces naturais, reconhecimento de gestos e de atividade, aprendizagem de máquina e tecnologias de sensores que são usados para capturar a entrada do meio ambiente para processamento posterior. Essas áreas são introduzidas e os conceitos básicos são descritos. O ambiente de desenvolvimento para o pré-processamento de dados e algoritmos de aprendizagem de máquina programada em Python é descrito e as principais bibliotecas são discutidas. A coleta dos fluxos de dados é explicada e é descrito o conjunto de dados utilizado. A arquitetura proposta de aprendizagem consiste em dois passos. O pré-processamento dos dados de entrada e a arquitetura de aprendizagem. O pré-processamento é limitado a três estratégias diferentes, que são combinadas para oferecer seis diferentes perfis de préprocessamento. No segundo passo, um Deep Belief Network é introduzido e os seus componentes são explicados. Com esta definição, 294 experimentos são realizados com diferentes configurações. As variáveis que são alteradas são as definições de pré-processamento, a estrutura de camadas do modelo, a taxa de aprendizagem de pré-treino e a taxa de aprendizagem de afinação. A avaliação dessas experiências mostra que a abordagem de utilização de uma arquitetura ... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Activity recognition from computer vision plays an important role in research towards applications like human computer interfaces, intelligent environments, surveillance or medical systems. In this work, a gesture recognition system based on a deep learning architecture is proposed. It is used to analyze the performance when trained with multi-modal input data on an Italian sign language dataset. The underlying research area is a field called human-machine interaction. It combines research on natural user interfaces, gesture and activity recognition, machine learning and sensor technologies, which are used to capture the environmental input for further processing. Those areas are introduced and the basic concepts are described. The development environment for preprocessing data and programming machine learning algorithms with Python is described and the main libraries are discussed. The gathering of the multi-modal data streams is explained and the used dataset is outlined. The proposed learning architecture consists of two steps. The preprocessing of the input data and the actual learning architecture. The preprocessing is limited to three different strategies, which are combined to offer six different preprocessing profiles. In the second step, a Deep Belief network is introduced and its components are explained. With this setup, 294 experiments are conducted with varying configuration settings. The variables that are altered are the preprocessing settings, the layer structure of the model, the pretraining and the fine-tune learning rate. The evaluation of these experiments show that the approach of using a deep learning architecture on an activity or gesture recognition task yields acceptable results, but has not yet reached a level of maturity, which would allow to use the developed models in serious applications.
Mestre
Salman, Ahmad. "Learning speaker-specific characteristics with deep neural architecture". Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/learning-speakerspecific-characteristics-with-deep-neural-architecture(24acb31d-2106-4e52-80ab-6c649838026a).html.
Texto completoGoh, Hanlin. "Learning deep visual representations". Paris 6, 2013. http://www.theses.fr/2013PA066356.
Texto completoRecent advancements in the areas of deep learning and visual information processing have presented an opportunity to unite both fields. These complementary fields combine to tackle the problem of classifying images into their semantic categories. Deep learning brings learning and representational capabilities to a visual processing model that is adapted for image classification. This thesis addresses problems that lead to the proposal of learning deep visual representations for image classification. The problem of deep learning is tackled on two fronts. The first aspect is the problem of unsupervised learning of latent representations from input data. The main focus is the integration of prior knowledge into the learning of restricted Boltzmann machines (RBM) through regularization. Regularizers are proposed to induce sparsity, selectivity and topographic organization in the coding to improve discrimination and invariance. The second direction introduces the notion of gradually transiting from unsupervised layer-wise learning to supervised deep learning. This is done through the integration of bottom-up information with top-down signals. Two novel implementations supporting this notion are explored. The first method uses top-down regularization to train a deep network of RBMs. The second method combines predictive and reconstructive loss functions to optimize a stack of encoder-decoder networks. The proposed deep learning techniques are applied to tackle the image classification problem. The bag-of-words model is adopted due to its strengths in image modeling through the use of local image descriptors and spatial pooling schemes. Deep learning with spatial aggregation is used to learn a hierarchical visual dictionary for encoding the image descriptors into mid-level representations. This method achieves leading image classification performances for object and scene images. The learned dictionaries are diverse and non-redundant. The speed of inference is also high. From this, a further optimization is performed for the subsequent pooling step. This is done by introducing a differentiable pooling parameterization and applying the error backpropagation algorithm. This thesis represents one of the first attempts to synthesize deep learning and the bag-of-words model. This union results in many challenging research problems, leaving much room for further study in this area
Kola, Ramya Sree. "Generation of synthetic plant images using deep learning architecture". Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18450.
Texto completoXiao, Yao. "Vehicle Detection in Deep Learning". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91375.
Texto completoMaster of Science
Computer vision techniques are becoming increasingly popular. For example, face recognition is used to help police find criminals, vehicle detection is used to prevent drivers from serious traffic accidents, and written word recognition is used to convert written words into printed words. With the rapid development of vehicle detection given the use of deep learning techniques, there are still concerns about the performance of state-of-the art vehicle detection techniques. For example, state-of-the-art vehicle detectors are restricted by the large variation of scales. People working on vehicle detection are developing techniques to solve this problem. This thesis proposes an advanced vehicle detection model, utilizing deep learning techniques to detect the potential objects’ information.
Tsardakas, Renhuldt Nikos. "Protein contact prediction based on the Tiramisu deep learning architecture". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231494.
Texto completoAtt kunna bestämma proteiners struktur har tillämpningar inom både medicin och industri. Såväl experimentell bestämning av proteinstruktur som prediktion av densamma är svårt. Predicerad kontakt mellan olika delar av ett protein underlättar prediktion av proteinstruktur. Under senare tid har djupinlärning använts för att bygga bättre modeller för kontaktprediktion. Den här uppsatsen beskriver en ny djupinlärningsmodell för prediktion av proteinkontakter, TiramiProt. Modellen bygger på djupinlärningsarkitekturen Tiramisu. TiramiProt tränas och utvärderas på samma data som kontaktprediktionsmodellen PconsC4. Totalt tränades modeller med 228 olika hyperparameterkombinationer till konvergens. Mätt över ett flertal olika parametrar presterar den färdiga TiramiProt-modellen resultat i klass med state-of-the-art-modellerna PconsC4 och RaptorX-Contact. TiramiProt finns tillgängligt som ett Python-paket samt en Singularity-container via https://gitlab.com/nikos.t.renhuldt/TiramiProt.
Fayyazifar, Najmeh. "Deep learning and neural architecture search for cardiac arrhythmias classification". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2553.
Texto completoQian, Xiaoye. "Wearable Computing Architecture over Distributed Deep Learning Hierarchy: Fall Detection Study". Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case156195574310931.
Texto completoÄhdel, Victor. "On the effect of architecture on deep learning based features for homography estimation". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233194.
Texto completoNyckelpunkts-detektion och deskriptor-skapande är det första steget av homografi och essentiell matris estimering, vilket i sin tur används inom Visuell Odometri och Visuell SLAM. Det här arbetet utforskar effekten (i form av snabbhet och exakthet) av användandet av olika djupinlärnings-arkitekturer för sådana nyckelpunkter. De hel-faltade nätverken – med huvuden för både detektorn och deskriptorn – tränas genom en existerande själv-handledd metod, där korrespondenser fås genom kända slumpmässigt valda homografier. En ny strategi för valet av negativa korrespondenser för deskriptorns träning presenteras, vilket möjliggör mer flexibilitet i designen av arkitektur. Den nya strategin visar sig vara väsentlig då den möjliggör nätverk som presterar bättre än den lärda baslinjen utan någon kostnad i inferenstid. Varieringen av modellstorleken leder till en kompromiss mellan snabbhet och exakthet, och medan alla modellerna presterar bättre än ORB i homografi-estimering, så är det endast de större modellerna som närmar sig SIFTs prestanda; där de presterar 1-7% sämre. Att träna längre och med ytterligare typer av data kanske ger tillräcklig förbättring för att prestera bättre än SIFT. Även fast de minsta modellerna är 3× snabbare och använder 50× färre parametrar än den lärda baslinjen, så kräver de fortfarande 3× så mycket tid som SIFT medan de presterar runt 10-30% sämre. Men det finns fortfarande utrymme för förbättring genom optimeringsmetoder som övergränsar ändringar av arkitekturen, som till exempel kvantisering, vilket skulle kunna göra metoden snabbare än SIFT.
Libros sobre el tema "Deep learning architecture"
Calin, Ovidiu. Deep Learning Architectures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36721-3.
Texto completoPedrycz, Witold y Shyi-Ming Chen, eds. Deep Learning: Concepts and Architectures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31756-0.
Texto completoPedrycz, Witold y Shyi-Ming Chen, eds. Development and Analysis of Deep Learning Architectures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31764-5.
Texto completoKang, Mingu, Sujan Gonugondla y Naresh R. Shanbhag. Deep In-memory Architectures for Machine Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35971-3.
Texto completoMitchell, Laura, Vishnu Subramanian y Sri Yogesh K. Deep Learning with Pytorch 1. x: Implement Deep Learning Techniques and Neural Network Architecture Variants Using Python, 2nd Edition. Packt Publishing, Limited, 2019.
Buscar texto completoShi, Cong, Ji Liu y Xichuan Zhou. Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture. Elsevier, 2022.
Buscar texto completoDeep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture. Elsevier, 2022.
Buscar texto completoBengio, Yoshua. Learning Deep Architectures for AI. Now Publishers Inc, 2009.
Buscar texto completoDecision Making Handbook: Engineering, IoT, Information Technology, Marketing, Architecture, Deep Learning, Data Mining,TR5, Excel Dashboard, Social Media, Business Development and Artificial Intelligence. Independently Published, 2022.
Buscar texto completoDaneshtalab, Masoud y Mehdi Modarressi, eds. Hardware Architectures for Deep Learning. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/pbcs055e.
Texto completoCapítulos de libros sobre el tema "Deep learning architecture"
Briot, Jean-Pierre, Gaëtan Hadjeres y François-David Pachet. "Architecture". En Deep Learning Techniques for Music Generation, 51–114. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-70163-9_5.
Texto completoTripathi, Ashish, Shraddha Upadhaya, Arun Kumar Singh, Krishna Kant Singh, Arush Jain, Pushpa Choudhary y Prem Chand Vashist. "Deep Learning Architecture and Framework". En Deep Learning in Visual Computing and Signal Processing, 1–27. Boca Raton: Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003277224-1.
Texto completoWüthrich, Mario V. y Michael Merz. "Deep Learning". En Springer Actuarial, 267–379. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_7.
Texto completoKrig, Scott. "Feature Learning and Deep Learning Architecture Survey". En Computer Vision Metrics, 375–514. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33762-3_10.
Texto completoBlubaugh, David Allen, Steven D. Harbour, Benjamin Sears y Michael J. Findler. "Subsumption Cognitive Architecture". En Intelligent Autonomous Drones with Cognitive Deep Learning, 377–406. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-6803-2_10.
Texto completoKang, Mingu, Sujan Gonugondla y Naresh R. Shanbhag. "The Deep In-Memory Architecture (DIMA)". En Deep In-memory Architectures for Machine Learning, 7–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35971-3_2.
Texto completoGridin, Ivan. "Multi-trial Neural Architecture Search". En Automated Deep Learning Using Neural Network Intelligence, 185–256. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8149-9_4.
Texto completoGridin, Ivan. "One-Shot Neural Architecture Search". En Automated Deep Learning Using Neural Network Intelligence, 257–318. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8149-9_5.
Texto completoYe, Andre. "Successful Neural Network Architecture Design". En Modern Deep Learning Design and Application Development, 327–400. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7413-2_6.
Texto completoHou, June-Hao y Chi-Li Cheng. "Reconstructing Photogrammetric 3D Model by Using Deep Learning". En Formal Methods in Architecture, 295–304. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57509-0_27.
Texto completoActas de conferencias sobre el tema "Deep learning architecture"
Kaskavalci, Halil Can y Sezer Goren. "A Deep Learning Based Distributed Smart Surveillance Architecture using Edge and Cloud Computing". En 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00009.
Texto completoWistuba, Martin. "Practical Deep Learning Architecture Optimization". En 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2018. http://dx.doi.org/10.1109/dsaa.2018.00037.
Texto completoKakanakova, Irina y Stefan Stoyanov. "Outlier Detection via Deep Learning Architecture". En CompSysTech'17: 18th International Conference on Computer Systems and Technologies. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3134302.3134337.
Texto completoB, Sangeetha, Senthil Prabha R y Ravitha Rajalakshmi N. "Deep Learning Architecture For Fruit Classification". En Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India. EAI, 2021. http://dx.doi.org/10.4108/eai.7-12-2021.2314483.
Texto completoHervert Hernandez, Esau, Yan Cao y Nasser Kehtarnavaz. "Deep learning architecture search for real-time image denoising". En Real-Time Image Processing and Deep Learning 2022, editado por Nasser Kehtarnavaz y Matthias F. Carlsohn. SPIE, 2022. http://dx.doi.org/10.1117/12.2620349.
Texto completoSharify, Sayeh, Alberto Delmas Lascorz, Mostafa Mahmoud, Milos Nikolic, Kevin Siu, Dylan Malone Stuart, Zissis Poulos y Andreas Moshovos. "Laconic deep learning inference acceleration". En ISCA '19: The 46th Annual International Symposium on Computer Architecture. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307650.3322255.
Texto completoSetyanto, Arief, Kusrini Kusrini, Theopilus Bayu Sasongko, Adhitya Bagasmiwa Permana y Andhy Panca Saputra. "Efficient Deep Learning Architecture for Facemask Detection". En 2021 4th International Conference on Information and Communications Technology (ICOIACT). IEEE, 2021. http://dx.doi.org/10.1109/icoiact53268.2021.9564011.
Texto completoGan, Yiming, Yuxian Qiu, Jingwen Leng, Minyi Guo y Yuhao Zhu. "Ptolemy: Architecture Support for Robust Deep Learning". En 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 2020. http://dx.doi.org/10.1109/micro50266.2020.00031.
Texto completoÇano, Erion y Maurizio Morisio. "A deep learning architecture for sentiment analysis". En ICGDA '18: 2018 the International Conference on Geoinformatics and Data Analysis, ICGDA '18. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3220228.3220229.
Texto completoDe Hertog, Dirk y Anaïs Tack. "Deep Learning Architecture for Complex Word Identification". En Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-0539.
Texto completoInformes sobre el tema "Deep learning architecture"
Cooper, Alexis, Xin Zhou, Scott Heidbrink y Daniel Dunlavy. Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models. Office of Scientific and Technical Information (OSTI), septiembre de 2020. http://dx.doi.org/10.2172/1668457.
Texto completoCooper, Alexis, Xin Zhou, Daniel Dunlavy y Scott Heidbrink. Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models. Office of Scientific and Technical Information (OSTI), septiembre de 2020. http://dx.doi.org/10.2172/1668929.
Texto completoTayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, enero de 2022. http://dx.doi.org/10.31979/mti.2022.2014.
Texto completoPettit, Chris y D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), junio de 2021. http://dx.doi.org/10.21079/11681/41034.
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