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24th Annual conference on neural Information processing Systems 2010, NIPS 2010

作者： Duchi, John C. Agarwal, Alekh Wainwright, Martin J. Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA 94720-1776 United States Department of Statistics University of California Berkeley Berkeley CA 94720-1776 United States

ISBN: (纸本)9781617823800

The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions using only local computation and communication. We develop and analyze distributed algorithms based on dual averaging of subgradients, and provide sharp bounds on their convergence rates as a function of the network size and topology. Our analysis clearly separates the convergence of the optimization algorithm itself from the effects of communication constraints arising from the network structure. We show that the number of iterations required by our algorithm scales inversely in the spectral gap of the network. The sharpness of this prediction is confirmed both by theoretical lower bounds and simulations for various networks.

关键词： Functions

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Application of Fuzzy neural network in Multi-Maneuvering Target Tracking

Application of Fuzzy Neural Network in Multi-Maneuvering Tar...

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2nd International Asia conference on Informatics in Control, Automation and Robotics (CAR)

作者： Hua, Jin North China Elect Power Univ Sch Comp & Technol Beijing Peoples R China

ISBN: (纸本)9781424451944

In this paper, a new method combined fuzzy theory and neural network was proposed, which was employed to solve multi-maneuvering target tracking. Multi-maneuvering target tracking is a process, which manages measure information to maintain currently state estimate. The fields of fuzzy sets and neural network have made rapid progress in recent years. neural networks are nonlinear network of self-organization and self-learning, they possess the capabilities of large-scale parallel processing, distributed information, neural network have important influence on the revolution of traditional target tracking theory. The training of fuzzy rule-based systems by using the learning ability of neural network can improve the expression ability of the network. Combining the fuzzy theory and neural network is a new approach to solve multi-maneuvering target tracking.

关键词： fuzzy theory neural network multi-maneuvering target tracking

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Learning Task Specific distributed Paragraph Representations Using a 2-Tier Convolutional neural network 22nd

Learning Task Specific Distributed Paragraph Representations...

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22nd International conference on neural Information processing (ICONIP)

作者： Chen, Tao Xu, Ruifeng He, Yulan Wang, Xuan Harbin Inst Technol Shenzhen Grad Sch Shenzhen Engn Lab Performance Robots Digital Stag Shenzhen Peoples R China Aston Univ Sch Engn & Appl Sci Birmingham B4 7ET W Midlands England

ISBN: (纸本)9783319265322;9783319265315

We introduce a type of 2-tier convolutional neural network model for learning distributed paragraph representations for a special task (e.g. paragraph or short document level sentiment analysis and text topic categorization). We decompose the paragraph semantics into 3 cascaded constitutes: word representation, sentence composition and document composition. Specifically, we learn distributed word representations by a continuous bag-of-words model from a large unstructured text corpus. Then, using these word representations as pre-trained vectors, distributed task specific sentence representations are learned from a sentence level corpus with task-specific labels by the first tier of our model. Using these sentence representations as distributed paragraph representation vectors, distributed paragraph representations are learned from a paragraph-level corpus by the second tier of our model. It is evaluated on DBpedia ontology classification dataset and Amazon review dataset. Empirical results show the effectiveness of our proposed learning model for generating distributed paragraph representations.

关键词： Natural language processing distributed representation Convolutional neural network

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Field Independent Target Classification Analysis in distributed Acoustic Sensing Systems 27

Field Independent Target Classification Analysis in Distribu...

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27th Signal processing and Communications Applications conference (SIU)

作者： Maral, Hakan Aktas, Metin ASELSAN Ulasim Guvenlik Enerji & Otomasyon Sistemleri UGES Sekt Baskanligi Ankara Turkey

ISBN: (纸本)9781728119045

In this paper, we are able to focus on the fiber-optic distributed acoustic systems required to isolate the oil and gas pipeline, remote areas, facilities and lines in the area. Threatening performances of network structures of different complexity and depth. All measurements, the criteria for working independently from the field, the results obtained and the results obtained and on-site troubleshooting methods in different geographical areas. It analyzes the performance results and shows a high performance network in the field in real time.

关键词： distributed acoustic sensing phase-OTDR deep learning convolutional neural networks CNN threat detection threat classification

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neural Speech Separation Using Spatially distributed Microphones 21

Neural Speech Separation Using Spatially Distributed Microph...

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Interspeech conference

作者： Wang, Dongmei Chen, Zhuo Yoshioka, Takuya Microsoft One Microsoft Way Redmond WA 98052 USA

ISBN: (纸本)9781713820697

This paper proposes a neural network based speech separation method using spatially distributed microphones. Unlike with traditional microphone array settings, neither the number of microphones nor their spatial arrangement is known in advance, which hinders the use of conventional multi-channel speech separation neural networks based on fixed size input. To overcome this, a novel network architecture is proposed that interleaves inter-channel processing layers and temporal processing layers. The inter-channel processing layers apply a self-attention mechanism along the channel dimension to exploit the information obtained with a varying number of microphones. The temporal processing layers are based on a bidirectional long short term memory (BLSTM) model and applied to each channel independently. The proposed network leverages information across time and space by stacking these two kinds of layers alternately. Our network estimates time-frequency (TF) masks for each speaker, which are then used to generate enhanced speech signals either with TF masking or beamforming. Speech recognition experimental results show that the proposed method significantly outperforms baseline multi-channel speech separation systems.

关键词： distributed microphone array ad hoc array speech separation overlapped speech distant speech recognition

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distributed inference in dynamical systems 19

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20th Annual conference on neural Information processing Systems, NIPS 2006

作者： Funiak, Stanislav Guestrin, Carlos Paskin, Mark Sukthankar, Rahul Carnegie Mellon University United States Google United States Intel Research United States

ISBN: (纸本)9780262195683

We present a robust distributed algorithm for approximate probabilistic inference in dynamical systems, such as sensor networks and teams of mobile robots. Using assumed density filtering, the network nodes maintain a tractable representation of the belief state in a distributed fashion. At each time step, the nodes coordinate to condition this distribution on the observations made throughout the network, and to advance this estimate to the next time step. In addition, we identify a significant challenge for probabilistic inference in dynamical systems: message losses or network partitions can cause nodes to have inconsistent beliefs about the current state of the system. We address this problem by developing distributed algorithms that guarantee that nodes will reach an informative consistent distribution when communication is re-established. We present a suite of experimental results on real-world sensor data for two real sensor network deployments: one with 25 cameras and another with 54 temperature sensors.

关键词： Dynamical systems

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Prioritising Security Tests on Large- Scale and distributed Software Development Projects by Using Self-organised Maps 1

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23rd International conference on neural Information processing (ICONIP)

作者： Alvares, Marcos de Lima Neto, Fernando Buarque Marwala, Tshilidzi Univ Johannesburg Johannesburg South Africa Univ Pernambuco Recife PE Brazil

ISBN: (数字)9783319466811

ISBN: (纸本)9783319466811;9783319466804

Large-scale and distributed software development initiatives demand a systematic testing process in order to prevent failures. Significant amount of resources are usually allocated on testing. Like any development and designing task, testing activities have to be prioritised in order to efficiently validate the produced code. By using source code complexity measurement, Computational Intelligence and Image processing techniques, this research presents a new approach to prioritise testing efforts on large-scale and distributed software projects. The proposed technique was validated by automatically highlighting sensitive code within the Linux device drivers source code base. Our algorithm was able to classify 3, 077 from 35, 091 procedures as critical code to be tested. We argue that the approach is general enough to prioritise test tasks of most critical large-scale and distributed developed software such as: Operating Systems, Enterprise Resource Planning and Content Management systems.

关键词： Enterprise resource planning

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Prediction of R in sinter process based on grey neural network algebra

Prediction of R in sinter process based on grey neural netwo...

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8th ACIS International conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed Computing/3rd ACIS International Workshop on Self-Assembling Wireless networks

作者： Wang Ai-min Song Qiang Wuhan Univ Technol Sch Comp Sci Wuhan 430070 Peoples R China Anyang Normal Univ Sch Comp Sci Anyang 455000 Peoples R China Anyang Inst Technol Dept Mech Engn Anyang 455002 Peoples R China

ISBN: (纸本)0769529097

A grey neural network model was proposed on the basis of the models. The fluctuation of data sequence is weakened by the grey theory and the neural network is capable of processing non-linear adaptable information, and the GNN is a combination of those advantages. The results reveal, the alkalinity of sinter can be accurately predicted through this model by reference to small sample and information. It was concluded that the GNN model is effective with the advantages of high precision, less requirement of samples and comparatively simple calculation.

关键词： alkalinity of sinter grey neural network prediction the sintering process grey model

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neural network MODELS

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PARALLEL COMPUTING 1988年第1-3期8卷 71-83页

作者： FORREST, BM ROWETH, D STROUD, N WALLACE, DJ WILSON, GV Physics Department University of Edinburgh Edinburgh United Kingdom EH9 3JZ

The remarkable processing capabilities of the nervous system must derive at least in part from the large numbers of neurons participating (roughly 1010), since the timescales involved are of the order of milliseconds, rather than the nanoseconds of modern computers. We summarise common features of the neural network models which attempt to capture this behaviour and describe the many levels of parallelism which they exhibit. A range of models has been implemented on the SIMD (ICL distributed Array Processor) and MIMD (Meiko Computing Surface) hardware at Edinburgh. Examples include: (i) training algorithms in the context of the Hopfield net, with specific application to the storage of words and text with content-addressable memory; (ii) the back-propagation training algorithm for the multi-layer perception; (iii) image restoration with Hopfield and Tank analogue neurons, and (iv) the Durbin and Willshaw elastic net, as applied to the travelling salesman problem.

关键词： neural networks content-addressable memory analogue neurons elastic net travelling salesman problem

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Introducing a Novel Data Management Approach for distributed Large Scale Data processing in Future Computer Clouds

Introducing a Novel Data Management Approach for Distributed...

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18th International conference on neural Information processing (ICONIP)

作者： Basirat, Amir H. Khan, Asad I. Monash Univ Clayton Sch IT Melbourne Vic 3004 Australia

ISBN: (纸本)9783642249570;9783642249587

Deployment of pattern recognition applications for large-scale data sets is an open issue that needs to be addressed. In this paper, an attempt is made to explore new methods of partitioning and distributing data, that is, resource virtualization in the cloud by fundamentally re-thinking the way in which future data management models will need to be developed on the Internet. The work presented here will incorporate content-addressable memory into Cloud data processing to entail a large number of loosely-coupled parallel operations resulting in vastly improved performance. Using a lightweight associative memory algorithm known as distributed Hierarchical Graph Neuron (DHGN), data retrieval/processing can be modeled as pattern recognition/matching problem, conducted across multiple records and data segments within a single-cycle, utilizing a parallel approach. The proposed model envisions a distributed data management scheme for large-scale data processing and database updating that is capable of providing scalable real-time recognition and processing with high accuracy while being able to maintain low computational cost in its function.

关键词： distributed Data processing neural network Data Mining Associative Computing Cloud Computing

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