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检索条件"任意字段=Conference on Neural Network and Distributed Processing"

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Parallel distributed gradient descent and ascent methods

Parallel distributed gradient descent and ascent methods

引用

IJCNN International Joint conference on neural networks

作者： Takefuji, Yoshiyasu Case Western Research Univ Dep of Electr Eng Cleveland OH USA

Summary form only given, as follows. A new parallel distributed processing architecture called an entropy machine (EM) is proposed. This machine, which is based on an artificial neural network composed of massive neurons and interconnections, is used for solving a variety of NP-complete optimization problems. The EM performs either the parallel distributed gradient descent method or gradient ascent method to search for minima or maxima.

关键词： Systems Science and Cybernetics

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DNN-BASED distributed MULTICHANNEL MASK ESTIMATION FOR SPEECH ENHANCEMENT IN MICROPHONE ARRAYS

DNN-BASED DISTRIBUTED MULTICHANNEL MASK ESTIMATION FOR SPEEC...

引用

IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Furnon, Nicolas Serizel, Romain Illina, Irina Essid, Slim Univ Lorraine CNRS INRIA Loria F-54000 Nancy France Inst Polytech Paris Telecom Paris LTCI Palaiseau France

ISBN: (纸本)9781509066315

Multichannel processing is widely used for speech enhancement but several limitations appear when trying to deploy these solutions in the real world. distributed sensor arrays that consider several devices with a few microphones is a viable solution which allows for exploiting the multiple devices equipped with microphones that we are using in our everyday life. In this context, we propose to extend the distributed adaptive node-specific signal estimation approach to a neural network framework. At each node, a local filtering is performed to send one signal to the other nodes where a mask is estimated by a neural network in order to compute a global multichannel Wiener filter. In an array of two nodes, we show that this additional signal can be leveraged to predict the masks and leads to better speech enhancement performance than when the mask estimation relies only on the local signals.

关键词： Speech enhancement microphone arrays distributed processing

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Attention Approximates Sparse distributed Memory 35

Attention Approximates Sparse Distributed Memory

引用

35th conference on neural Information processing Systems (NeurIPS)

作者： Bricken, Trenton Pehlevan, Cengiz Harvard Univ Syst Synthet & Quantitat Biol Cambridge MA 02138 USA Harvard Univ Appl Math Cambridge MA 02138 USA

ISBN: (纸本)9781713845393

While Attention has come to be an important mechanism in deep learning, there remains limited intuition for why it works so well. Here, we show that Transformer Attention can be closely related under certain data conditions to Kanerva's Sparse distributed Memory (SDM), a biologically plausible associative memory model. We confirm that these conditions are satisfied in pre-trained GPT2 Transformer models. We discuss the implications of the Attention-SDM map and provide new computational and biological interpretations of Attention.

关键词： Deep learning

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Building representative and balanced datasets of OpenMP parallel regions 29

Building representative and balanced datasets of OpenMP para...

引用

29th Euromicro International conference on Parallel, distributed and network-Based processing (PDP)

作者： Alcaraz, Jordi Sleder, Steven TehraniJamsaz, Ali Sikora, Anna Jannesari, Ali Sorribes, Joan Cesar, Eduardo Univ Autonoma Barcelona Cerdanyola Del Valles Spain Iowa State Univ Ames IA USA

ISBN: (纸本)9781665414555

Incorporating machine learning into automatic performance analysis and tuning tools is a promising path to tackle the increasing heterogeneity of current HPC applications. However, this introduces the need for generating balanced and representative datasets of parallel applications' executions. This work proposes a methodology for building datasets OpenMP parallel code regions patterns. It allows for determining whether a given code region covers a unique part of the pattern input space not covered by the patterns already included in the dataset. The proposed methodology uses hardware performance counter;to represent the execution of the region. which is referred to as the region signature for a given number of cores. Then, a complete representation of the region is built by joining the signatures for every different thread configuration in the system. Next, correlation analysis is performed between this representation and the representation of all the patterns already in the training set. Finally, H . this correlation is helm a given threshold. the region is considered to cover a unique part of the pattern input space and is subsequently added to the dataset. For validating this methodology, an example dataset, obtained from well known benchmarks, has been used to train a carefully designed neural network model to demonstrate that it is able to classify . different patterns of OpenMP parallel regions.

关键词： hardware performance counters machine learning artificial neural networks parallel applications OpeaMP

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Finite Precision Implementation of Random Vector Functional-Link networks 22

Finite Precision Implementation of Random Vector Functional-...

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2017 22nd International conference on Digital Signal processing (DSP)

作者： Rosato, Antonello Altilio, Rosa Panella, Massimo Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9781538618950

The increasing amount of data to be processed coming from multiple sources, as in the case of sensor networks, and the need to cope with constraints of security and privacy, make necessary the use of computationally efficient techniques on simple and cheap hardware architectures often distributed in pervasive scenarios. Random Vector Functional-Link is a neural network model usually adopted for processing distributed big data, but no constraints have been considered so far to deal with limited hardware resources. This paper is focused on implementing a modified version of the Random Vector Functional-Link network with finite precision arithmetic, in order to make it suited to hardware architectures even based on a simple microcontroller. A genetic optimization is also proposed to ensure that the overall performance is comparable with standard software implementations. The numerical results prove the efficacy of the proposed approach.

关键词： Hardware Optimization Genetic algorithms Computer architecture neural networks Quantization (signal) Training

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Extracting Features from Both the INput and the Output of a Convolutional neural network to Detect distributed Denial of Service Attacks 17

Extracting Features from Both the INput and the Output of a ...

引用

17th IEEE International conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Ghanbari, Maryam Kinsner, Witold Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

ISBN: (纸本)9781538633601

distributed Denial-of-Service (DDoS) attacks are serious threats to a smart grid infrastructure services' availability, and can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). An improved version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the non-pure fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract distinguishing features during data pre-processing. A support vector machine (SVM) was used for post data-processing. The implementation detected the DDoS attack with 87.35% accuracy.

关键词： Cyber and physical network security anomaly detection method distributed denial of service attack discrete wavelet transform variance fractal dimension convolutional neural network

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Acceleration in distributed Sparse Regression 36

Acceleration in Distributed Sparse Regression

引用

36th conference on neural Information processing Systems (NeurIPS)

作者： Maros, Marie Scutari, Gesualdo Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781713871088

We study acceleration for distributed sparse regression in high-dimensions, which allows the parameter size to exceed and grow faster than the sample size. When applicable, existing distributed algorithms employing acceleration perform poorly in this setting, theoretically and numerically. We propose a new accelerated distributed algorithm suitable for high-dimensions. The method couples a suitable instance of accelerated Nesterov's proximal gradient with consensus and gradient-tracking mechanisms, aiming at estimating locally the gradient of the empirical loss while enforcing agreement on the local estimates. Under standard assumptions on the statistical model and tuning parameters, the proposed method is proved to globally converge at linear rate to an estimate that is within the statistical precision of the model. The iteration complexity scales as O(root kappa), while the communications per iteration are at most (O) over tilde (log m/(1-rho)), where kappa is the restricted condition number of the empirical loss, m is the number of agents, and rho is an element of[0,1) measures the network connectivity. As by-product of our design, we also report an accelerated method for high-dimensional estimations over master-worker architectures, which is of independent interest and compares favorably with existing works.

关键词： Iterative methods

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Federated Learning Based Resource Allocation for Wireless Communication networks 30

Federated Learning Based Resource Allocation for Wireless Co...

引用

30th European Signal processing conference (EUSIPCO)

作者： Behmandpoor, Pourya Patrinos, Panagiotis Moonen, Marc Katholieke Univ Leuven STADIUS Ctr Dynam Syst Signal Proc & Data Analyt Dept Elect Engn ESAT Leuven Belgium

ISBN: (纸本)9789082797091

In this paper we introduce federated learning (FL) based resource allocation (RA) for wireless communication networks, where users cooperatively train a RA policy in a distributed scenario. The RA policy for each user is represented by a local deep neural network (DNN), which has the same structure for all users. Each DNN monitors local measurements and outputs a power allocation to the user. The proposed approach is model-free;each user is responsible for training its own DNN to maximize the sum rate (SR) and communicates with the server to aggregate its local DNN with other DNNs. More importantly, each user needs to probe only its own data rate as a distributed reward function and communications with the server once in a while. Simulations show that the proposed approach enables conventional deep learning (DL) based RA methods to not only use their policy in a distributed scenario, but also to (re)train their policy in time-varying environments in a model-free distributed manner without needing a computationally complex server.

关键词： Deep neural network federated learning distributed reward wireless communication resource allocation

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Learning a hierarchical belief network of independent factor analyzers

Learning a hierarchical belief network of independent factor...

引用

12th Annual conference on neural Information processing Systems (NIPS)

作者： Attias, H Univ Calif San Francisco Sloan Ctr Theoret Neurobiol San Francisco CA 94143 USA

ISBN: (纸本)0262112450

Many belief networks have been proposed that are composed of binary units. However, for tasks such as object and speech recognition which produce real-valued data, binary network models are usually inadequate. Independent component analysis (ICA) learns a model from real data, but the descriptive power of this model is severly limited. We begin by describing the independent factor analysis (IFA) technique, which overcomes some of the limitations of ICA. We then create a multilayer network by cascading singlelayer IFA models. At each level, the IFA network extracts real-valued latent variables that are non-linear functions of the input data with a highly adaptive functional form, resulting in a hierarchical distributed representation of these data. Whereas exact maximum-likelihood learning of the network is intractable, we derive an algorithm that maximizes a lower bound on the likelihood, based on a variational approach.

关键词： Independent component analysis

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DELAY-ORIENTED distributed SCHEDULING USING GRAPH neural networkS 47

DELAY-ORIENTED DISTRIBUTED SCHEDULING USING GRAPH NEURAL NET...

引用

47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Zhao, Zhongyuan Verma, Gunjan Swami, Ananthram Segarra, Santiago Rice Univ Houston TX 77251 USA US Armys DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9781665405409

In wireless multi-hop networks, delay is an important metric for many applications. However, the max-weight scheduling algorithms in the literature typically focus on instantaneous optimality, in which the schedule is selected by solving a maximum weighted independent set (MWIS) problem on the interference graph at each time slot. These myopic policies perform poorly in delay-oriented scheduling, in which the dependency between the current backlogs of the network and the schedule of the previous time slot needs to be considered. To address this issue, we propose a delay-oriented distributed scheduler based on graph convolutional networks (GCNs). In a nutshell, a trainable GCN module generates node embeddings that capture the network topology as well as multi-step lookahead backlogs, before calling a distributed greedy MWIS solver. In small- to medium-sized wireless networks with heterogeneous transmit power, where a few central links have many interfering neighbors, our proposed distributed scheduler can outperform the myopic schedulers based on greedy and instantaneously optimal MWIS solvers, with good generalizability across graph models and minimal increase in communication complexity.

关键词： Maximum weighted independent set graph neural networks distributed scheduling latency

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