Search Results - "Parallel and Distributed Algorithms"

Scheduling Policies for Federated Learning in Wireless Networks by Yang, Howard H., Liu, Zuozhu, Quek, Tony Q. S., Poor, H. Vincent

“…Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing…”
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Reducing distance computations for distance-based outliers by Angiulli, Fabrizio, Basta, Stefano, Lodi, Stefano, Sartori, Claudio

“…•The paper introduces the FastSolvingSet algorithm to discover outliers.•This algorithm computes the distance based outliers with no approximation.•The…”
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Scalable and Robust Demand Response With Mixed-Integer Constraints by Seung-Jun Kim, Giannakis, Georgios B.

“…A demand response (DR) problem is considered entailing a set of devices/subscribers, whose operating conditions are modeled using mixed-integer constraints…”
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Statistical properties of a class of randomized binary search algorithms by Xia, Ye

“…In this paper, we analyze the statistical properties of a randomized binary search algorithm and its variants. These algorithms have applications in caching…”
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Novel Control Approaches for Demand Response With Real-Time Pricing Using Parallel and Distributed Consensus-Based ADMM by Nguyen, Dinh Hoa, Azuma, Shun-Ichi, Sugie, Toshiharu

“…This paper studies the automated demand response (DR) problem in smart grids equipped with information and communication technology networks, where power…”
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Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness by Pang, Guohao, Zhu, Xiaorong

“…With the explosive growth of smart mobile devices in wireless networks, the increasing computational power of mobile chips and the growing concern for personal…”
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Distributed Strategies for Mining Outliers in Large Data Sets by Angiulli, F., Basta, S., Lodi, S., Sartori, C.

“…We introduce a distributed method for detecting distance-based outliers in very large data sets. Our approach is based on the concept of outlier detection…”
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GPU Strategies for Distance-Based Outlier Detection by Angiulli, Fabrizio, Basta, Stefano, Lodi, Stefano, Sartori, Claudio

“…The process of discovering interesting patterns in large, possibly huge, data sets is referred to as data mining, and can be performed in several flavours,…”
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Robust Federated Learning With Noisy Labeled Data Through Loss Function Correction by Chen, Li, Ang, Fan, Chen, Yunfei

“…Federated learning (FL) is a communication-efficient machine learning paradigm to leverage distributed data at the network edge. Nevertheless, FL usually fails…”
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A data structure perspective to the RDD-based Apriori algorithm on Spark by Singh, Pankaj, Singh, Sudhakar, Mishra, P. K., Garg, Rakhi

“…During the recent years, a number of efficient and scalable frequent itemset mining algorithms for big data analytics have been proposed by many researchers…”
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Iterated dilated convolutional neural networks for word segmentation by He. H., Yang, X, Wu L., Wang, G

“…The latest development of neural word segmentation is governed by bi-directional Long Short-Term Memory Networks (Bi-LSTMs) that utilize Recurrent Neural…”
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Networks of Picture Processors with Filtering Based on Evaluation Sets as Solvers for Cryptographic Puzzles Based on Random Multivariate Quadratic Equations by Jiménez, Karina Paola, Gómez-Canaval, Sandra, Villanueva-Polanco, Ricardo, Suazo, Silvia Martín

“…Networks of picture processors is a massively distributed and parallel computational model inspired by the evolutionary cellular processes, which offers…”
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Distributed Nearest Neighbor-Based Condensation of Very Large Data Sets by Angiulli, F., Folino, G.

“…In this work, the parallel fast condensed nearest neighbor (PFCNN) rule, a distributed method for computing a consistent subset of a very large data set for…”
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A Comparative Study of Four Parallel and Distributed PSO Methods by Vanneschi, Leonardo, Codecasa, Daniele, Mauri, Giancarlo

“…We present four new parallel and distributed particle swarm optimization methods consisting in a genetic algorithm whose individuals are co-evolving swarms, an…”
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Efficiency Optimization Method of Wireless Federated Learning Considering Computational Capability and Channel State by Pang, Guohao, Li, Fengguo, Zhu, Xiaorong

“…Due to the explosive growth in the variety of smart mobile terminals in wireless networks, the increasing computing capability of mobile chips, and the…”
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Solution techniques for multi-physics problems with application to computational nonlinear aeroelasticity by Soulaı¨mani, Azzeddine, Feng, Zhengkun, Ben Haj Ali, Amin

“…This paper presents an efficient parallel-distributed methodology for solving multi-physic problems. This methodology is based on functional and geometric…”
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Practice and Experience in using Parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures by Riedel, Morris, Sedona, Rocco, Barakat, Chadi, Einarsson, Petur, Hassanian, Reza, Cavallaro, Gabriele, Book, Matthias, Neukirchen, Helmut, Lintermann, Andreas

“…We observe a continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., 'big data') that…”
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Practice and Experience in Using Parallel and Scalable Machine Learning in Remote Sensing from HPC Over Cloud to Quantum Computing by Riedel, Morris, Cavallaro, Gabriele, Benediktsson, Jon Atli

“…Using computationally efficient techniques for transforming the massive amount of Remote Sensing (RS) data into scientific understanding is critical for Earth…”
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Techniques for Graph Analytics on Big Data by Nisar, M. Usman, Fard, Arash, Miller, John A.

“…Graphs enjoy profound importance because of their versatility and expressivity. They can be effectively used to represent social networks, web search engines…”
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Design and Evaluation of an HPC-based Expert System to speed-up Retail Data Analysis using Residual Networks Combined with Parallel Association Rule Mining and Scalable Recommenders by Barakat, C., Riedel, M., Brynjolfsson, S., Cavallaro, G., Busch, J., Sedona, R.

“…Given the Covid-19 pandemic, the retail industry shifts many business models to enable more online purchases that produce large transaction data quantities…”
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