Relevant bibliographies by topics / Neural networks; Visual information

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural networks; Visual information'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Neural networks; Visual information"

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Hertz, J. A., T. W. Kjær, E. N. Eskandar, and B. J. Richmond. "MEASURING NATURAL NEURAL PROCESSING WITH ARTIFICIAL NEURAL NETWORKS." International Journal of Neural Systems 03, supp01 (1992): 91–103. http://dx.doi.org/10.1142/s0129065792000425.

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We show how to use artificial neural networks as a quantitative tool in studying real neuronal processing in the monkey visual system. Training a network to classify neuronal signals according to the stimulus that elicited them permits us to calculate the information transmitted by these signals. We illustrate this for neurons in the primary visual cortex with measurements of the information transmitted about visual stimuli and for cells in inferior temporal cortex with measurements of information about behavioral context. For the latter neurons we also illustrate how artificial neural network
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Kawato, Mitsuo, Takatoshi Ikeda, and Sei Miyake. "Learning in neural networks for visual information processing." Journal of the Institute of Television Engineers of Japan 42, no. 9 (1988): 918–24. http://dx.doi.org/10.3169/itej1978.42.918.

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Seeland, Marco, and Patrick Mäder. "Multi-view classification with convolutional neural networks." PLOS ONE 16, no. 1 (2021): e0245230. http://dx.doi.org/10.1371/journal.pone.0245230.

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Humans’ decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object’s class from just a single image showing an object. Especially for challenging classification problems, the visual information conveyed by a single image may be insufficient for an accurate decision. We propose a classification scheme that relies on fusing visual information captured through images depicting the same object from multiple perspectives. Convolutional neural networks are used to extr
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MAINZER, KLAUS. "CELLULAR NEURAL NETWORKS AND VISUAL COMPUTING." International Journal of Bifurcation and Chaos 13, no. 01 (2003): 1–6. http://dx.doi.org/10.1142/s0218127403006534.

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Brain-like information processing has become a challenge to modern computer science and chip technology. The CNN (Cellular Neural Network) Universal Chip is the first fully programmable industrial-sized brain-like stored-program dynamic array computer which dates back to an invention of Leon O. Chua and Lin Yang in Berkeley in 1988. Since then, many papers have been written on the mathematical foundations and technical applications of CNN chips. They are already used to model artificial, physical, chemical, as well as living biological systems. CNN is now a new computing paradigm of interdisci
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Hartono, Pitoyo. "A transparent cancer classifier." Health Informatics Journal 26, no. 1 (2018): 190–204. http://dx.doi.org/10.1177/1460458218817800.

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Recently, many neural network models have been successfully applied for histopathological analysis, including for cancer classifications. While some of them reach human–expert level accuracy in classifying cancers, most of them have to be treated as black box, in which they do not offer explanation on how they arrived at their decisions. This lack of transparency may hinder the further applications of neural networks in realistic clinical settings where not only decision but also explainability is important. This study proposes a transparent neural network that complements its classification d
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Et. al., K. P. Moholkar,. "Visual Question Answering using Convolutional Neural Networks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 170–75. http://dx.doi.org/10.17762/turcomat.v12i1s.1602.

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The ability of a computer system to be able to understand surroundings and elements and to think like a human being to process the information has always been the major point of focus in the field of Computer Science. One of the ways to achieve this artificial intelligence is Visual Question Answering. Visual Question Answering (VQA) is a trained system which can answer the questions associated to a given image in Natural Language. VQA is a generalized system which can be used in any image-based scenario with adequate training on the relevant data. This is achieved with the help of Neural Netw
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Deng, Yu Qiao, and Ge Song. "A Verifiable Visual Cryptography Scheme Using Neural Networks." Advanced Materials Research 756-759 (September 2013): 1361–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1361.

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This paper proposes a new verifiable visual cryptography scheme for general access structures using pi-sigma neural networks (VVCSPSN), which is based on probabilistic signature scheme (PSS), which is considered as security and effective verification method. Compared to other high-order networks, PSN has a highly regular structure, needs a much smaller number of weights and less training time. Using PSNs capability of large-scale parallel classification, VCSPSN reduces the information communication rate greatly, makes best known upper bound polynomial, and distinguishes the deferent informatio
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Merilaita, Sami. "Artificial neural networks and the study of evolution of prey coloration." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1479 (2007): 421–30. http://dx.doi.org/10.1098/rstb.2006.1969.

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In this paper, I investigate the use of artificial neural networks in the study of prey coloration. I briefly review the anti-predator functions of prey coloration and describe both in general terms and with help of two studies as specific examples the use of neural network models in the research on prey coloration. The first example investigates the effect of visual complexity of background on evolution of camouflage. The second example deals with the evolutionary choice of defence strategy, crypsis or aposematism. I conclude that visual information processing by predators is central in evolu
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Wolfrum, Philipp, and Christoph von der Malsburg. "What Is the Optimal Architecture for Visual Information Routing?" Neural Computation 19, no. 12 (2007): 3293–309. http://dx.doi.org/10.1162/neco.2007.19.12.3293.

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Analyzing the design of networks for visual information routing is an underconstrained problem due to insufficient anatomical and physiological data. We propose here optimality criteria for the design of routing networks. For a very general architecture, we derive the number of routing layers and the fanout that minimize the required neural circuitry. The optimal fanout l is independent of network size, while the number k of layers scales logarithmically (with a prefactor below 1), with the number n of visual resolution units to be routed independently. The results are found to agree with data
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Medvedev, Viktor, Gintautas Dzemyda, Olga Kurasova, and Virginijus Marcinkevičius. "Efficient Data Projection for Visual Analysis of Large Data Sets Using Neural Networks." Informatica 22, no. 4 (2011): 507–20. http://dx.doi.org/10.15388/informatica.2011.339.

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Dissertations / Theses on the topic "Neural networks; Visual information"

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Song, Yue. "Towards Multi-Scale Visual Explainability for Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281359.

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Explainability methods seek to find out visual explanations for neural network decisions. Existing techniques mainly fall into two categories: backpropagation- based methods and occlusion-based methods. The former category selectively highlights the computed gradients, while the latter occludes the input to maximally confuse the classifier and visualize the distinct regions. Motivated by the occlusion methods, we propose an explainability model which to our knowledge is the first attempt to extract multi-scale explanations by perturbing the intermediate representations. Furthermore, we present
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Newman, Rhys A. "Automatic learning in computer vision." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390526.

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Mayer, Nikolaus [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Synthetic training data for deep neural networks on visual correspondence tasks." Freiburg : Universität, 2020. http://d-nb.info/1216826692/34.

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Yavari, Najib. "Few-Shot Learning with Deep Neural Networks for Visual Quality Control: Evaluations on a Production Line." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283119.

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Having a well representative and adequate amount of data samples plays an important role in the success of deep learning algorithms used for image recognition. On the other hand, collecting and manually labeling a large-scale dataset requires a great deal of human interaction which in turn is very timeconsuming. In this thesis project, we explore the possibilities of new deeplearning approaches used for image recognition that do not require a big amount of data. Since Few-Shot Learning (FSL) models are known to be the most promising approach to tackle the problem of not having an adequate data
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Aboudib, Ala. "Neuro-inspired Architectures for the Acquisition and Processing of Visual Information." Thesis, Télécom Bretagne, 2016. http://www.theses.fr/2016TELB0419/document.

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L'apprentissage automatique et la vision par ordinateur sont deux sujets de recherche d'actualité. Des contributions clés à ces domaines ont été les fruits de longues années d'études du cortex visuel et de la fonction des réseaux cérébraux. Dans cette thèse, nous nous intéressons à la conception des architectures neuro-inspirées pour le traitement de l'information sur trois niveaux différents du cortex visuel. Au niveau le plus bas, nous proposons un réseau de neurones pour l'acquisition des signaux visuels. Ce modèle est étroitement inspiré par le fonctionnement et l'architecture de la retine
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Ajamlou, Kevin, and Max Sonebäck. "Multimodal Convolutional Graph Neural Networks for Information Extraction from Visually Rich Documents." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445457.

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Monotonous and repetitive tasks consume a lot of time and resources in businesses today and the incentive to fully or partially automate said tasks, in order to relieve office workers and increase productivity in the industry, is therefore high. One such task is to process and extract information from Visually Rich Documents (VRD:s), e.g., documents where the visual attributes contain important information about the contents of the document. A lot of recent studies have focused on information extraction from invoices, where graph based convolutional nerual networks have shown a lot of promise
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Michler, Frank [Verfasser], and Thomas [Akademischer Betreuer] Wachtler. "Self-Organization of Spiking Neural Networks for Visual Object Recognition / Frank Michler ; Betreuer: Thomas Wachtler." Marburg : Philipps-Universität Marburg, 2020. http://d-nb.info/1204199876/34.

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Dercksen, Vincent Jasper [Verfasser]. "Visual computing techniques for the reconstruction and analysis of anatomically realistic neural networks / Vincent Jasper Dercksen." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1081935391/34.

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Tong, Song. "Informatics Approaches for Understanding Human Facial Attractiveness Perception and Visual Attention." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/264679.

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京都大学<br>新制・課程博士<br>博士(情報学)<br>甲第23398号<br>情博第767号<br>新制||情||131(附属図書館)<br>京都大学大学院情報学研究科知能情報学専攻<br>(主査)教授 熊田 孝恒, 教授 西田 眞也, 教授 齋木 潤, 准教授 延原 章平<br>学位規則第4条第1項該当<br>Doctor of Informatics<br>Kyoto University<br>DFAM
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Salem, Tawfiq. "Learning to Map the Visual and Auditory World." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/86.

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The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabil
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Books on the topic "Neural networks; Visual information"

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Information routing, correspondence finding, and object recognition in the brain. Springer-Verlag, 2010.

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Venkatesan, Ragav, and Baoxin Li. Convolutional Neural Networks in Visual Computing. CRC Press, 2017. http://dx.doi.org/10.4324/9781315154282.

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Rosandich, Ryan G. Intelligent visual inspection: Using artificial neural networks. Chapman & Hall, 1997.

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Rosandich, Ryan G. Intelligent Visual Inspection: Using artificial neural networks. Springer US, 1996.

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Zhang, Xiang-Sun. Neural Networks in Optimization. Springer US, 2000.

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Information theoretic neural computation. World Scientific, 2002.

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Govindaraju, R. S. Artificial Neural Networks in Hydrology. Springer Netherlands, 2000.

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Kaynak, Okyay, Ethem Alpaydin, Erkki Oja, and Lei Xu, eds. Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2.

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T, Roska, ed. Cellular neural networks and visual computing: Foundation and applications. Cambridge University Press, 2002.

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T. V. S. M. olde Scheper. Chaos and information in dynamic neural networks. Oxford Brookes University, 2002.

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Book chapters on the topic "Neural networks; Visual information"

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Yu, Ying, Bin Wang, and Liming Zhang. "Hebbian-Based Neural Networks for Bottom-Up Visual Attention Systems." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_1.

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Turcsany, Diana, and Andrzej Bargiela. "Learning Local Receptive Fields in Deep Belief Networks for Visual Feature Detection." In Neural Information Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_58.

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Chen, Yanyin, Xing Chen, Huibin Tan, et al. "Cross-Layer Convolutional Siamese Network for Visual Tracking." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04179-3_13.

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Lu, H. B., and Y. J. Zhang. "Detecting Abrupt Scene Change Using Neural Network." In Visual Information and Information Systems. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48762-x_37.

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Yuan, Zejian, Lei Yang, Yanyun Qu, Yuehu Liu, and Xinchun Jia. "A Boosting SVM Chain Learning for Visual Information Retrieval." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_156.

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Mallot, Hanspeter A., and Werner Von Seelen. "Why Cortices? Neural Networks for Visual Information Processing." In Visuomotor Coordination. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-0897-1_11.

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Ning, Xiaodong, and Lixiong Liu. "Level Set Based Online Visual Tracking via Convolutional Neural Network." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70090-8_29.

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Sjöberg, Mats, Jorma Laaksonen, Matti Pöllä, and Timo Honkela. "Retrieval of Multimedia Objects by Combining Semantic Information from Visual and Textual Descriptors." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_8.

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Zhang, Jinjian, and Xiaodong Gu. "Desert Vehicle Detection Based on Adaptive Visual Attention and Neural Network." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_47.

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Li, Jie, and Yue Zhou. "Visual Saliency Based Blind Image Quality Assessment via Convolutional Neural Network." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70136-3_58.

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Conference papers on the topic "Neural networks; Visual information"

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Canziani, Alfredo, and Eugenio Culurciello. "Visual attention with deep neural networks." In 2015 49th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2015. http://dx.doi.org/10.1109/ciss.2015.7086900.

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Xiao, Youping, Ravi Rao, Guilermo Cecchi, and Ehud Kaplan. "Cortical representation of information about visual attributes: one network or many?" In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371228.

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Koprinkova-Hristova, Petia, Simona Nedelcheva, Nadejda Bocheva, et al. "STDP Training of Hierarchical Spike Timing Model of Visual Information Processing." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207598.

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Belkaid, Marwen, Nicolas Cuperlier, and Philippe Gaussier. "Combining local and global visual information in context-based neurorobotic navigation." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727851.

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Rassadin, Alexandr G., and Andrey V. Savchenkov. "Compressing deep convolutional neural networks in visual emotion recognition." In Information Technology and Nanotechnology 2017. Samara University, 2017. http://dx.doi.org/10.18287/1613-0073-2017-1901-207-213.

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Song Ge, Peng Changgen, and Miao Xuelan. "Visual Cryptography Scheme Using Pi-sigma Neural Networks." In 2008 International Symposium on Information Science and Engineering (ISISE). IEEE, 2008. http://dx.doi.org/10.1109/isise.2008.208.

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Deng, Yuqiao, and Ge Song. "A Verifiable Visual Cryptography Scheme Using Neural Networks." In 2nd International Conference on Computer and Information Applications (ICCIA 2012). Atlantis Press, 2012. http://dx.doi.org/10.2991/iccia.2012.27.

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Kounavis, Michael E., Joel Morrissette, Sadagopan Srinivasan, and Raj Yavatkar. "Detecting non-transient anomalies in visual information using neural networks." In 2011 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2011. http://dx.doi.org/10.1109/iscc.2011.5983853.

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Guobao Xu, Yixin Yin, Lu Yin, Yanshuang Hao, and Zhenyu Wang. "Visual information processing using cellular neural networks for mobile robot." In 2007 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2007. http://dx.doi.org/10.1109/gsis.2007.4443432.

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Hou, Jen-Cheng, Syu-Siang Wang, Ying-Hui Lai, et al. "Audio-visual speech enhancement using deep neural networks." In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2016. http://dx.doi.org/10.1109/apsipa.2016.7820732.

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Reports on the topic "Neural networks; Visual information"

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Koch, Christof. Controlling the Flow of Visual Information through the Lateral Geniculate Nucleus: From Single Cells to Neural Networks. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada250578.

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Levitan, Herbert. Microcomputer-Based Data Acquisition, Analysis and Control of Information Processing by Neural Networks. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177170.

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Grossberg, Stephen. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada189981.

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Lugo-Garcia, Nidza, Damien P. Kuffler, and Rosa E. Blanco. Neural Networks: Structure and Repair. Part 1. Ground Squirrel Visual System. Part 2. Formation, Maintenance and Plasticity of Synaptic Connections. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada282420.

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati
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