Bibliografia publikacji pracowników
Państwowej Szkoły Wyższej w Białej Podlaskiej

Baza tworzona przez Bibliotekę Akademii Bialskiej im. Jana Pawła II.

Zapytanie: ADAPTIVE TRAINING STEP
Liczba odnalezionych rekordów: 2

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Autorzy: , , Adam adaptive training step deep learning RBM ReLU unsupervised learning.
Szczegóły:
Miejsce wydania: 1662-453X003An analytical approach for unsupervised learning rate estimation using rectified linear
Wydawca: Q003
Rok wydania: 009999.0002024-04-10, 12:49
Charakterystyka formalna: he area of neural networks. In this paper mathematical expressions to adaptive learning step calculation for RBM with ReLU transfer function are proposed. As a result, we can automatically estimate the step size that minimizes the loss function of the neural network and correspondingly update the learning step in every iteration. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The proposed technique for adaptive learning rate estimation is compared with the existing constant step and Adam methods in terms of generalization ability and loss function. We demonstrate that the proposed approach provides better performance.^aAdam^aadaptive training step^adeep learning^aRBM^aReLU^aunsupervised learning
Charakterystyka wg MNiSW:
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DOI:

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Nr opisu:
Autorzy: , K 003 Institute of Electrical and Electronics Engineers Inc. 003 Adaptive Learning Rate for Unsupervised Learning of Deep Neural Networks 2023 International Joint Conference on Neural Networks (IJCNN) Proceedings, 18-23 June 2023, Gold Coast, Australia P. 1-6 [Piscataway]978-1-6654-8867-9 2022/2023 10.1109/IJCNN54540.2023.10191642 Chodyka, Marta adaptive training step In this paper an approach for adaptive learning step calculation using ReLU transfer function in neural network is proposed. This adaptive learning rate aims to automatically choose the step size that minimizes the objective function of neural network. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The main contribution of this paper is a novel technique for adaptive learning rate calculation, if we use ReLU transfer function. The experiments in data compression datasets show that proposed approach provides better generalization capability (test set accuracy) and permits to choose the learning rate automatically.Lichograj, Piotr deep learning unsupervised learning.
Miejsce wydania: 003Adaptive Learning Rate for Unsupervised Learning of Deep Neural Networks2023 International Joint Conference on Neural
Wydawca: K003
Rok wydania: 2023-09-14, 13:18
Strony zajęte przez pracę:
Charakterystyka formalna:
Charakterystyka wg MNiSW:
Wskaźnik Impact Factor ISI: twork is proposed. This adaptive learning rate aims to automatically choose the step size that minimizes the objective function of neural network. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The main contribution of this paper is a novel technique for adaptive learning rate calculation, if we use ReLU transfer function. The experiments in data compression datasets show that proposed approach provides better generalization capability (test set accuracy) and permits to choose the learning rate automatically.^aLichograj, Piotr^cy^adeep learning^aunsupervised learning
Słowa kluczowe ang.: ;

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