Relevant bibliographies by topics / Dense networks

Contents

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

Academic literature on the topic 'Dense networks'

Author: Grafiati
Published: 25 January 2025
Last updated: 31 July 2025

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Journal articles on the topic "Dense networks"

1

SIPPER, MOSHE. "CLUSTER-DENSE NETWORKS." International Journal of Modern Physics C 19, no. 06 (2008): 939–46. http://dx.doi.org/10.1142/s0129183108012650.

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Small-world networks, exhibiting short nodal distances and high clustering, and scale-free networks, typified by a scale-free, power-law node-degree distribution, have been shown to be widespread both in natural and artificial systems. We propose a new type of network — cluster-dense network — characterized by multiple clusters that are highly intra-connected and sparsely inter-connected. Employing two graph-theoretic measures — local density and relative density — we demonstrate that such networks are prevalent in the world of networks.
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Campbell, Lowell. "Dense group networks." Discrete Applied Mathematics 37-38 (July 1992): 65–71. http://dx.doi.org/10.1016/0166-218x(92)90125-t.

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Wang, Wei, Yutao Li, Ting Zou, Xin Wang, Jieyu You, and Yanhong Luo. "A Novel Image Classification Approach via Dense-MobileNet Models." Mobile Information Systems 2020 (January 6, 2020): 1–8. http://dx.doi.org/10.1155/2020/7602384.

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As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. The new network structure can make full use of the output feature maps generated by the previous convolution layers in den
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Athanasiadou, Georgia E., Panagiotis Fytampanis, Dimitra A. Zarbouti, George V. Tsoulos, Panagiotis K. Gkonis, and Dimitra I. Kaklamani. "Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures." Electronics 9, no. 2 (2020): 339. http://dx.doi.org/10.3390/electronics9020339.

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The 5G radio networks have introduced major changes in terms of service requirements and bandwidth allocation compared to cellular networks to date and hence, they have made the fundamental radio planning problem even more complex. In this work, the focus is on providing a generic analysis for this problem with the help of a proper multi-objective optimization algorithm that considers the main constraints of coverage, capacity and cost for high-capacity scenarios that range from dense to ultra-dense mmWave 5G standalone small-cell network deployments. The results produced based on the above an
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Zou, Kingsley Jun, and Kristo Wenjie Yang. "Network synchronization for dense small cell networks." IEEE Wireless Communications 22, no. 2 (2015): 108–17. http://dx.doi.org/10.1109/mwc.2015.7096293.

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Ge, Xiaohu, Song Tu, Guoqiang Mao, Cheng-Xiang Wang, and Tao Han. "5G Ultra-Dense Cellular Networks." IEEE Wireless Communications 23, no. 1 (2016): 72–79. http://dx.doi.org/10.1109/mwc.2016.7422408.

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F., Aguiló, F., E. E., Simó, and M. M., Zaragozá. "On Dense Triple-Loop Networks." Electronic Notes in Discrete Mathematics 10 (November 2001): 261–64. http://dx.doi.org/10.1016/s1571-0653(04)00406-8.

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Roy, Saptarshi, Titas Chanda, Tamoghna Das, Aditi Sen(De), and Ujjwal Sen. "Deterministic quantum dense coding networks." Physics Letters A 382, no. 26 (2018): 1709–15. http://dx.doi.org/10.1016/j.physleta.2018.04.033.

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Kamel, Mahmoud, Walaa Hamouda, and Amr Youssef. "Ultra-Dense Networks: A Survey." IEEE Communications Surveys & Tutorials 18, no. 4 (2016): 2522–45. http://dx.doi.org/10.1109/comst.2016.2571730.

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Oyakhire, Omuwa, and Koichi Gyoda. "Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks." Future Internet 12, no. 3 (2020): 47. http://dx.doi.org/10.3390/fi12030047.

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In mobile ad hoc networks, network nodes cooperate by packet forwarding from the source to the destination. As the networks become denser, more control packets are forwarded, thus consuming more bandwidth and may cause packet loss. Recently, game theory has been applied to address several problems in mobile ad hoc networks like energy efficiency. In this paper, we apply game theory to reduce the control packets in dense networks. We choose a proactive routing protocol, Optimized Link State Routing (OLSR) protocol. We consider two strategies in this method: willingness_always and willingness_ne
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Dissertations / Theses on the topic "Dense networks"

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Bulashenko, A. V., and I. V. Zabegaloff. "5G ultra dense networks." Thesis, Sumy State University, 2017. http://essuir.sumdu.edu.ua/handle/123456789/66962.

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The traffic demands predicted by 2030 are up to 10,000 times greater than in 2010 and end-service users will need to support 100 Mbps. One of the key developments that will provide this demand is the deployment of very dense and multi layered networks.
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Hettinger, Christopher James. "Hyperparameters for Dense Neural Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7531.

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Neural networks can perform an incredible array of complex tasks, but successfully training a network is difficult because it requires us to minimize a function about which we know very little. In practice, developing a good model requires both intuition and a lot of guess-and-check. In this dissertation, we study a type of fully-connected neural network that improves on standard rectifier networks while retaining their useful properties. We then examine this type of network and its loss function from a probabilistic perspective. This analysis leads to a new rule for parameter initialization a
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Sharma, Sachin. "Integrated Backhaul Management for Ultra-Dense Network Deployment." Thesis, KTH, Kommunikationssystem, CoS, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-159447.

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Mobile data traffic is expected to increase substantially in the coming years, with data rates 1000 times higher by 2020, having media and content as the main drivers together with a plethora of new end-user services that will challenge existing networks. Concepts and visions associated with the ICT evolution like the network society, 50 billion connected devices, Industrial Internet, Tactile Internet, etc., exemplifies the range of new services that the networks will have to handle. These new services impose extreme requirement to the network like high capacity, low latency, reliability, secu
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Bansal, Tarun. "Network-Centric Mechanisms for Performance Improvement in Dense Wireless Networks." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397749798.

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Baudin, Émilie. "Raptor Codes for Super-Dense Networks." Thesis, KTH, Signalbehandling, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140523.

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In this project, we investigate the performance of Raptor codes as candidatesfor channel coding for the wireless communication between access nodes.Very high data-rates are used, and processing uses more resources than transmission.Therefore, we need fast encoding and decoding algorithms for thechannel coding. Raptor codes have linear encoding and decoding times, andcan have very small overhead if they are properly designed. Hence, they arepossible candidates. We have implemented an encoding and decoding algorithm for Raptorcodes, as well as an environment for simulation. The system requiremen
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Peng, Jixian, and 彭继娴. "Macroscopic characteristics of dense road networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/195994.

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In the continuum modeling of traffic networks, a macroscopic cost-flow function (MCF) and macroscopic fundamental diagram (MFD) can be used to represent the fundamental relationships between traffic quantities such as speed, flow, and density. The MCF governs the steady-state cost-flow relationship, whereas the MFD represents the instantaneous inter-relationship between speed, flow, and density of traffic streams. This thesis explores the influence of network topologies on the MCF and MFD. The Hong Kong road system is divided into unit-sized road networks with various physical characteristics
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Cortes-Pena, Luis Miguel. "Optimizing dense wireless networks of MIMO links." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52254.

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Wireless communication systems have exploded in popularity over the past few decades. Due to their popularity, the demand for higher data rates by the users, and the high cost of wireless spectrum, wireless providers are actively seeking ways to improve the spectral efficiency of their networks. One promising technique to improve spectral efficiency is to equip the wireless devices with multiple antennas. If both the transmitter and receiver of a link are equipped with multiple antennas, they form a multiple-input multiple-output (MIMO) link. The multiple antennas at the nodes provide degrees
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Papadopoulos, Aris. "Energy-efficient routing for dense wireless sensor networks." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540663.

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Giménez, Colás Sonia. "Ultra Dense Networks Deployment for beyond 2020 Technologies." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/86204.

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A new communication paradigm is foreseen for beyond 2020 society, due to the emergence of new broadband services and the Internet of Things era. The set of requirements imposed by these new applications is large and diverse, aiming to provide a ubiquitous broadband connectivity. Research community has been working in the last decade towards the definition of the 5G mobile wireless networks that will provide the proper mechanisms to reach these challenging requirements. In this framework, three key research directions have been identified for the improvement of capacity in 5G: the increase of t
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Wambi, Paul James. "Efficient energy management in ultra-dense wireless networks." Master's thesis, Faculty of Engineering and the Built Environment, 2019. http://hdl.handle.net/11427/30999.

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The increase in demand for more network capacity has led to the evolution of wireless networks from being largely Heterogeneous (Het-Nets) to the now existing Ultra-dense (UDNs). In UDNs, small cells are densely deployed with the goal of shortening the physical distance between the base stations (BSs) and the UEs, so as to support more user equipment (UEs) at peak times while ensuring high data rates. Compared to Het-Nets, Ultra-dense networks (UDNs) have many advantages. These include, more network capacity, higher flexibility to routine configurations, and more suitability to achieve load-ba
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Books on the topic "Dense networks"

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Hurter, Fabian Peter. GNSS meteorology in spatially dense networks. Schweizerische Geodätische Kommission, 2014.

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Duong, Trung Q., Xiaoli Chu, and Himal A. Suraweera. Ultra-dense Networks for 5G and Beyond. John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781119473756.

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Chen, Shanzhi, Fei Qin, Bo Hu, Xi Li, Zhonglin Chen, and Jiamin Liu. User-Centric Ultra-Dense Networks for 5G. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-61201-0.

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Denise, Pumain, and Saint-Julien Thérèse, eds. Urban networks in Europe: Réseaux urbains en Europe / édité par Denise Pumain and Thérèse Saint-Julien. J. Libbey Eurotext, 1996.

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Zhang, Haijun, Jemin Lee, Tony Q. S. Quek, and Chih-Lin I, eds. Ultra-dense Networks. Cambridge University Press, 2020. http://dx.doi.org/10.1017/9781108671323.

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Sun, Wen. Ultra-Dense Heterogeneous Networks. CRC Press LLC, 2022.

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Wong, Lawrence Wai-Choong, Haibin Zhang, Wen Sun, Chao Shen, and Nan Zhao. Ultra-Dense Heterogeneous Networks. Taylor & Francis Group, 2022.

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Wong, Lawrence Wai-Choong, Haibin Zhang, Wen Sun, Chao Shen, and Nan Zhao. Ultra-Dense Heterogeneous Networks. Taylor & Francis Group, 2022.

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Wong, Lawrence Wai-Choong, Haibin Zhang, Wen Sun, Chao Shen, and Nan Zhao. Ultra-Dense Heterogeneous Networks. CRC Press LLC, 2022.

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Wong, Lawrence Wai-Choong, Haibin Zhang, Wen Sun, Chao Shen, and Nan Zhao. Ultra-Dense Heterogeneous Networks. Taylor & Francis Group, 2022.

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Book chapters on the topic "Dense networks"

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Andrews, Jeffrey G., Abhishek K. Gupta, Ahmad Alammouri, and Harpreet S. Dhillon. "Dense Cellular Networks." In Synthesis Lectures on Learning, Networks, and Algorithms. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29743-4_5.

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Hvitfeldt, Emil, and Julia Silge. "Dense neural networks." In Supervised Machine Learning for Text Analysis in R. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003093459-13.

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Sun, Wen, Haibin Zhang, Nan Zhao, Chao Shen, and Lawrence Wai-Choong Wong. "Introduction." In Ultra-Dense Heterogeneous Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003148654-1.

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Sun, Wen, Haibin Zhang, Nan Zhao, Chao Shen, and Lawrence Wai-Choong Wong. "Promising Applications." In Ultra-Dense Heterogeneous Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003148654-5.

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Sun, Wen, Haibin Zhang, Nan Zhao, Chao Shen, and Lawrence Wai-Choong Wong. "Enabling Factors and Emerging Techniques." In Ultra-Dense Heterogeneous Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003148654-4.

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Sun, Wen, Haibin Zhang, Nan Zhao, Chao Shen, and Lawrence Wai-Choong Wong. "Resource and Interference Management." In Ultra-Dense Heterogeneous Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003148654-2.

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Sun, Wen, Haibin Zhang, Nan Zhao, Chao Shen, and Lawrence Wai-Choong Wong. "Summary and Future Work." In Ultra-Dense Heterogeneous Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003148654-6.

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Sun, Wen, Haibin Zhang, Nan Zhao, Chao Shen, and Lawrence Wai-Choong Wong. "Mobility Management." In Ultra-Dense Heterogeneous Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003148654-3.

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Das Sarma, Atish, Ashwin Lall, Danupon Nanongkai, and Amitabh Trehan. "Dense Subgraphs on Dynamic Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33651-5_11.

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Hosseinzadeh, Mohammad Mehdi. "Dense Subgraphs in Biological Networks." In SOFSEM 2020: Theory and Practice of Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38919-2_60.

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Conference papers on the topic "Dense networks"

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Li, Jun, Yongjun Chen, Lei Cai, Ian Davidson, and Shuiwang Ji. "Dense Transformer Networks for Brain Electron Microscopy Image Segmentation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/401.

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The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is th
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Mizuno, Takayuki, Kohki Shibahara, Takayuki Kobayashi, and Yutaka Miyamoto. "High-capacity Dense Space Division Multiplexed Multicore Fiber Transmission." In Photonic Networks and Devices. OSA, 2017. http://dx.doi.org/10.1364/networks.2017.netu2b.3.

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Alvarez, Pedro, Carlo Galiotto, Jonathan van de Belt, Danny Finn, Hamed Ahmadi, and Luiz DaSilva. "Simulating dense small cell networks." In 2016 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2016. http://dx.doi.org/10.1109/wcnc.2016.7565167.

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Reznik, Alex, Chunxuan Ye, Yuying Dai, Samian Kaur, and John Tomici. "Mobility management for dense networks." In 2011 34th IEEE Sarnoff Symposium. IEEE, 2011. http://dx.doi.org/10.1109/sarnof.2011.5876483.

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Niesen, Urs. "Interference alignment in dense wireless networks." In 2010 IEEE Information Theory Workshop on Information Theory (ITW). IEEE, 2010. http://dx.doi.org/10.1109/itwksps.2010.5503124.

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Shojaeifard, Arman, Khairi Ashour Hamdi, Emad Alsusa, Daniel K. C. So, and Jie Tang. "Optimal Deployment of Dense Cellular Networks." In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring). IEEE, 2016. http://dx.doi.org/10.1109/vtcspring.2016.7504384.

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Kamel, Mahmoud I., Walaa Hamouda, and Amr M. Youssef. "Multiple association in ultra-dense networks." In ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7511520.

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Tay, Wee-Peng, John N. Tsitsiklis, and Moe Z. Win. "Detection in Dense Wireless Sensor Networks." In 2007 IEEE Wireless Communications and Networking Conference. IEEE, 2007. http://dx.doi.org/10.1109/wcnc.2007.639.

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Ahsan Kazmi, S. M., Nguyen H. Tran, Tai Manh Ho, et al. "Resource management in dense heterogeneous networks." In 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2015. http://dx.doi.org/10.1109/apnoms.2015.7275383.

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Wang, Mingjie, Hao Cai, Xin Huang, and Minglun Gong. "ADNet: Adaptively Dense Convolutional Neural Networks." In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093431.

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Reports on the topic "Dense networks"

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Olariu, Stephan. Algorithmic Issues on Locally Dense Networks. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada328895.

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Meni, Mackenzie, Ryan White, Michael Mayo, and Kevin Pilkiewicz. Entropy-based guidance of deep neural networks for accelerated convergence and improved performance. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49805.

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Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can
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Zhang, F., X. Zhang, A. Farrel, O. Gonzalez de Dios, and D. Ceccarelli. RSVP-TE Signaling Extensions in Support of Flexi-Grid Dense Wavelength Division Multiplexing (DWDM) Networks. RFC Editor, 2016. http://dx.doi.org/10.17487/rfc7792.

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Zhang, X., H. Zheng, R. Casellas, O. Gonzalez de Dios, and D. Ceccarelli. GMPLS OSPF-TE Extensions in Support of Flexi-Grid Dense Wavelength Division Multiplexing (DWDM) Networks. RFC Editor, 2018. http://dx.doi.org/10.17487/rfc8363.

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Hopkins, Deborah. CRADA Final Report: Thermal Design and Analysis Tools for Dense-Wavelength-Division-Multiplexed (DWDM) Optical Networks. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/1157016.

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Zhang, F., X. Fu, D. Ceccarelli, and I. Hussain. Framework and Requirements for GMPLS-Based Control of Flexi-Grid Dense Wavelength Division Multiplexing (DWDM) Networks. Edited by O. Gonzalez de Dios and R. Casellas. RFC Editor, 2015. http://dx.doi.org/10.17487/rfc7698.

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JW Monroe, MT Ritsche, M Franklin, and KE Kehoe. Comparison of Meteorological Measurements from Sparse and Dense Surface Observation Networks in the U.S. Southern Great Plains. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/948525.

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Monroe, J. W., M. T. Ritsche, M. Franklin, and K. E. Kehoe. Comparison of meteorological measurements from sparse and dense surface observational networks in the U.S. southern Great Plains. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/937018.

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Garg, Raveesh, Eric Qin, Francisco Martinez, et al. Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1821960.

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Dafflon, Baptiste, S. Wielandt, S. Uhlemann, et al. Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded AI. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769774.

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