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Wyświetlanie 1-4 z 4
Tytuł:
On training deep neural networks using a streaming approach
Autorzy:
Duda, Piotr
Jaworski, Maciej
Cader, Andrzej
Wang, Lipo
Powiązania:
https://bibliotekanauki.pl/articles/91796.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
deep learning
data streams
convolutional neural networks
strumienie danych
konwolucyjne sieci neuronowe
Opis:
In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 15-26
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A method of ensuring data integrity in a data stream management system
Autorzy:
Widera, M.
Wróbel, J.
Widera, A.
Matonia, A.
Powiązania:
https://bibliotekanauki.pl/articles/333295.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
zarządzanie integralnością systemu baz danych
biomedyczne strumienie danych i przetwarzanie sygnałów
database management system integrity
biomedical data stream and signal processing
Opis:
Assurance of data integrity is one of the prerequisites for each computer system. The paper presents a method enabling on-line maintenance of stream data set integrity. This method is implemented in a data stream management system prototype designed to find application in a biomedical monitoring system. In the case of medical computer systems assurance of data integrity is particularly important for documenting formal results and for the patient's safety.
Źródło:
Journal of Medical Informatics & Technologies; 2004, 8; MM141-148
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of the shoppers loyalty with aggregated data streams
Autorzy:
Nikulin, V.
Powiązania:
https://bibliotekanauki.pl/articles/91688.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
big data
data streams
data aggregation
classification
regression
shopping
loyalty
churn
marketing
business informatics
strumienie danych
agregacja danych
Klasyfikacja
regresja
zakupy
lojalność
maselnica
informatyka biznesu
Opis:
Consumer brands often offer discounts to attract new shoppers to buy their products. The most valuable customers are those who return after this initial incentive purchase. With enough purchase history, it is possible to predict which shoppers, when presented an offer, will buy a new item. While dealing with Big Data and with data streams in particular, it is a common practice to summarize or aggregate customers’ transaction history to the periods of few months. As an outcome, we compress the given huge volume of data, and transfer the data stream to the standard rectangular format. Consequently, we can explore a variety of practically or theoretically motivated tasks. For example, we can rank the given field of customers in accordance to their loyalty or intension to repurchase in the near future. This objective has very important practical application. It leads to preferential treatment of the right customers. We tested our model (with competitive results) online during Kaggle-based Acquire Valued Shoppers Challenge in 2014.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 2; 69-79
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance enhancement of CUDA applications by overlapping data transfer and Kernel execution
Autorzy:
Raju, K.
Chiplunkar, Niranjan N
Powiązania:
https://bibliotekanauki.pl/articles/1956064.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
CPU-GPU
high-performance computing
kernel
data transfer
CUDA streams
obliczenia wysokiej wydajności
jądro
transfer danych
strumienie CUDA
Opis:
The CPU-GPU combination is a widely used heterogeneous computing system in which the CPU and GPU have different address spaces. Since the GPU cannot directly access the CPU memory, prior to invoking the GPU function the input data must be available on the GPU memory. On completion of GPU function, the results of computation are transferred to CPU memory. The CPU-GPU data transfer happens through PCIExpress bus. The PCI-E bandwidth is much lesser than that of GPU memory. The speed at which the data is transferred is limited by the PCI-E bandwidth. Hence, the PCI-E acts as a performance bottleneck. In this paper two approaches are discussed to minimize the overhead of data transfer, namely, performing the data transfer while the GPU function is being executed and reducing the amount of data to be transferred to GPU. The effectiveness of these approaches on the execution time of a set of CUDA applications is realized using CUDA streams. The results of our experiments show that the execution time of applications can be minimized with the proposed approaches.
Źródło:
Applied Computer Science; 2021, 17, 3; 5-18
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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