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Network In Network [article]

Min Lin, Qiang Chen, Shuicheng Yan
2014 arXiv   pre-print
We propose a novel deep network structure called "Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field.  ...  The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer.  ...  Conclusions We proposed a novel deep network called "Network In Network" (NIN) for classification tasks.  ... 
arXiv:1312.4400v3 fatcat:bicbw4jwqnaazcszad2pev2dpa

In-network Neural Networks [article]

Giuseppe Siracusano, Roberto Bifulco
2018 arXiv   pre-print
We present N2Net, a system that implements binary neural networks using commodity switching chips deployed in network switches and routers.  ...  Our system shows that these devices can run simple neural network models, whose input is encoded in the network packets' header, at packet processing speeds (billions of packets per second).  ...  ., the key of a key-value store entry [10] , in network packets' headers.  ... 
arXiv:1801.05731v1 fatcat:zojoroijgfhyvop2tl7zhcq67m

Network monitoring in multicast networks using network coding

T. Ho, B. Leong, Yu-Han Chang, Yonggang Wen, R. Koetter
2005 Proceedings. International Symposium on Information Theory, 2005. ISIT 2005.  
In this paper we show how information contained in robust network codes can be used for passive inference of possible locations of link failures or losses in a network.  ...  For distributed randomized network coding, we bound the probability of being able to distinguish among a given set of failure events, and give some experimental results for one and two link failures in  ...  Knowledge of the original network topology and network code allows inference, from changes in the coefficient vectors obtained at the sinks, of possible locations of losses in the network.  ... 
doi:10.1109/isit.2005.1523691 dblp:conf/isit/HoLCWK05 fatcat:7dgnkyqgsvarrnrjtvkbper5ni

Network In Graph Neural Network [article]

Xiang Song and Runjie Ma and Jiahang Li and Muhan Zhang and David Paul Wipf
2021 arXiv   pre-print
Network In Graph Neural Network (NGNN ), that allows arbitrary GNN models to increase their model capacity by making the model deeper.  ...  Graph Neural Networks (GNNs) have shown success in learning from graph structured data containing node/edge feature information, with application to social networks, recommendation, fraud detection and  ...  Inspired by the Network-in-Network architecture [20] , we present Network-in-Graph Neural-Network (NGNN ), a model agnostic methodology that allows arbitrary GNN models to increase their model capacity  ... 
arXiv:2111.11638v1 fatcat:n5ud4qfibjf67ch4qm2ai6dboe

Network coding in star networks

S. M. Sadegh, Tabatabaei Yazdi, Serap A. Savari, Gerhard Kramer
2008 2008 IEEE International Symposium on Information Theory  
We investigate network coding in star networks with multiple unicast sessions.  ...  We use entropy arguments to upper bound the simultaneous rates of communication among the different nodes in the network and prove that in many cases, the optimal network code is related to the combinatorial  ...  Star networks have received considerable attention in the networking literature.  ... 
doi:10.1109/isit.2008.4595001 dblp:conf/isit/YazdiSK08 fatcat:q6lkhl5j6zbmnmqmne4cuy2tku

Networking activities in supply networks

Thomas Johnsen, Finn Wynstra, Jurong Zheng, Christine Harland, Richard Lamming
2000 Journal of Strategic Marketing  
Findings from two case studies are discussed, focusing on the process of networking in a set of relationships within each network.  ...  It is especially in the context of the recent developments within supply chain management and lean supply that our research into the creation and operation of supply networks should be seen.  ...  between individual relationships in the network.  ... 
doi:10.1080/096525400346231 fatcat:g6ombeyji5asfem3zhtgeu4e3q

Network economics in cognitive networks

Chunxiao Jiang, Yan Chen, K. J. Ray Liu, Yong Ren
2015 IEEE Communications Magazine  
In order to enhance the spectrum management efficiency in cellular networks, the concept of "cognitive cellular networks" was introduced.  ...  In this article, we consider the economic issues in cognitive cellular networks from the perspectives of game theoretic modeling and mechanism design.  ...  network time slots in order to better control interference.  ... 
doi:10.1109/mcom.2015.7105644 fatcat:dqqy777n3nadvozdwxjwhy35lm

Deep Residual Network in Network

Hmidi Alaeddine, Malek Jihene, Mario Versaci
2021 Computational Intelligence and Neuroscience  
This paper presents a new deep residual network in network (DrNIN) model that represents a deeper model of DNIN.  ...  Deep network in network (DNIN) model is an efficient instance and an important extension of the convolutional neural network (CNN) consisting of alternating convolutional layers and pooling layers.  ...  Abbreviations CNN: Convolutional neural network NIN: Network in network DNIN: Deep network in network DrNIN: Deep residual network in network MLP: Multilayer perceptron DMLPconv: Deep MLPconv Table  ... 
doi:10.1155/2021/6659083 pmid:33679966 pmcid:PMC7925065 fatcat:z5kcts7ssrb2ljeyqpzpmwjele

Network criticality in vehicular networks

Ali Tizghadam, Weiwei Li, Alberto Leon-Garcia
2012 Performance Evaluation Review  
importance in a graph.This results provides a basis for designing robust clustering algorithms for vehicular networks.  ...  Network criticality (resistance distance) is a graph-theoretic metric that quantifies network robustness, and that was originally designed to capture the effect of environmental changes in core communication  ...  in a resistive network [3] .  ... 
doi:10.1145/2425248.2425278 fatcat:uab7q2qwnjbhhn4zenbuyj7lwa

Managing Network Elements in the Computer Network

Nenad Jovanovic, Suzana Markovic, Oliver Popovic, Zoran Jovanovic
2010 International Journal of Computer and Electrical Engineering  
This paper describes the process of development network management application in the IT infrastructure using the Java Management Extension (JMX).  ...  They collect information about the managed objects, send the information for management application and manage with the managed devices, in accordance with the feedback that they receive from management  ...  Other tools in this layer focus on network planning and design.  ... 
doi:10.7763/ijcee.2010.v2.154 fatcat:5dzfp7mjkbhrrnc3shwv43b7d4

Network Coding in Wireless Queueing Networks: Tandem Network Case

Yalin Sagduyu, Anthony Ephremides
2006 2006 IEEE International Symposium on Information Theory  
In this paper, we compare the effects of the saturated and possibly emptying packet queues on wireless network coding (or plain routing as a special case) in a simple tandem network.  ...  Finally, we extend the analysis to non-cooperative network operation with selfish nodes competing for limited network resources.  ...  Finally, we analyze network coding and plain routing in non-cooperative network operation.  ... 
doi:10.1109/isit.2006.261831 dblp:conf/isit/SagduyuE06 fatcat:mob32cdvgbdcfbxvmouleaende

Networked Individual in Networked City: Reviewing Social Network in Transportation Literature

Fariya Sharmeen, Pauline van den Berg, Harry Timmermans
2014 Social Networking  
Networks and networking are predominant in individuals' social and professional life. Social media has made networking even simpler in virtual world.  ...  In order to improve our understanding of the role of social networks in social activity-travel behavior, a series  ...  Networks and networking are predominant in individuals' social and professional life. Social media has made networking even simpler in virtual world.  ... 
doi:10.4236/sn.2014.33019 fatcat:2ahtigs4krfknmt3otiillkoru

Putting the network in network interventions

Thomas W. Valente
2017 Proceedings of the National Academy of Sciences of the United States of America  
Consequently, we now know that selecting influential nodes in a network results in superior intervention effectiveness.  ...  However, other tactics such as respondent-driven sampling, network segmentation, network outreach, and network manipulation have been shown to be effective in some settings and some applications (2).  ...  In many settings and in many communities and populations, network data are quite easy to collect.  ... 
doi:10.1073/pnas.1712473114 pmid:28851836 pmcid:PMC5594703 fatcat:ywefs4yzmvffzfcwqc426ml3hu

Batch-normalized Maxout Network in Network [article]

Jia-Ren Chang, Yong-Sheng Chen
2015 arXiv   pre-print
The proposed network adopts the framework of the recently developed Network In Network structure, which slides a universal approximator, multilayer perceptron (MLP) with rectifier units, to exact features  ...  This paper reports a novel deep architecture referred to as Maxout network In Network (MIN), which can enhance model discriminability and facilitate the process of information abstraction within the receptive  ...  For enhancing model discriminability, the Network In Network (NIN) [18] model uses a sliding micro neural network, multilayer perceptron (MLP), to increase the nonlinearity of local patches in order  ... 
arXiv:1511.02583v1 fatcat:cldzkiis7fd3xew5222qbcxnai

Networking issues in wireless sensor networks

Deepak Ganesan, Alberto Cerpa, Wei Ye, Yan Yu, Jerry Zhao, Deborah Estrin
2004 Journal of Parallel and Distributed Computing  
While the set of challenges in sensor networks are diverse, we focus on fundamental networking challenges in this paper.  ...  that support attribute-based data naming, routing and in-network aggregation, (c) geographic routing challenges in networks where nodes know their locations, and (d) monitoring and maintenance of such  ...  In this paper, we will take a more in-depth look at networking challenges, including more recent techniques in this area. Sensor networks pose interesting challenges for networking research.  ... 
doi:10.1016/j.jpdc.2004.03.016 fatcat:lmxjjvefkjbkbgxv7rbpuu4gya
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