Screening hardware and volume factors in distributed machine learning algorithms on spark

Rodrigues, Jairson Barbosa; Vasconcelos, Germano C.; Maciel, Paulo

doi:10.1007/s00607-021-00965-3

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…The noise of Chinese-English parallel corpus is time-varying and can only be regarded as stable in a short period of time. Therefore, the short-term processing technology is the most basic technology to deal with the noise of Chinese-English parallel corpus [ 20 , 21 ].

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Section: Chinese-english Parallel Corpus Noise Processing Model Based On Multilayer Perceptron Genetic Algorithm Neural Networkmentioning

confidence: 99%

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

Tuo

Kong

et al. 2021

Computational Intelligence and Neuroscience

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This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese-English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win-win of time and efficiency.

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…”

Section: Chinese-english Parallel Corpus Noise Processing Model Based On Multilayer Perceptron Genetic Algorithm Neural Networkmentioning

confidence: 99%

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

Tuo

Kong

et al. 2021

Computational Intelligence and Neuroscience

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“…For this work, we modify a DOE factor screening 23,[40][41][42][43][44][45][46] approach for a very limited experimental budget to determine the principal fabrication variables, predict directions for material improvement, and verify the hypothesis laid out above. Using three levels each for both sugar size and packing input variables, and two levels for solvent, a complete exploration of the parameter space with a standard full factorial design 47 and a minimum number of replications would require at least 36 distinct experiments.…”

Section: Intentional Doe Design For Limited Data Scenariosmentioning

confidence: 99%

Towards tunable polymer foam fabrication: A case study to advance green materials development in limited data scenarios

et al. 2022

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Early phases of green material development can be accelerated by identifying driving factors that control material properties to understand potential tradeoffs. Full investigation of fabrication variables is often prohibitively expensive. We propose a pared-down design of experiments (DOE) approach to identify driving variables in limited data scenarios using tunable polydimethylsiloxane (PDMS) foams made via sacrificial templating as an example system. This new approach systematically determines the dependencies of porosity, transparency, and fluid flow by varying the template particle size and packing while using a more sustainable solvent. Factor screening identified template particle size and packing density as the driving factors for foam performance by controlling pore size and interconnectivity. The framework developed provides a robust, foundational understanding of how to green and tune a novel material's properties using an efficient and effective exploration of the design space. Recommendations for applying this method to a broad suite of experiments are provided.

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Distributed Analytics For Big Data: A Survey

Berloco,

Bevilacqua,

Colucci

2024

Neurocomputing

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Screening hardware and volume factors in distributed machine learning algorithms on spark

Cited by 5 publications

References 40 publications

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

[Retracted] Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese‐English Parallel Corpus Noise Processing

Towards tunable polymer foam fabrication: A case study to advance green materials development in limited data scenarios

Distributed Analytics For Big Data: A Survey

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