Towards Optimal Planning for Green, Smart, and Semantically Enriched Cultural Tours

Kotis, Konstantinos; Dimara, Asimina; Angelis, Sotiris; Michailidis, Panagiotis; Michailidis, Iakovos; Anagnostopoulos, Christos‐Nikolaos; Krinidis, Stelios; Kosmatopoulos, Elias B.

doi:10.3390/smartcities6010007

Cited by 1 publication

(1 citation statement)

References 35 publications

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“…Highly advanced Artificial Intelligence (AI), along with its associated sub-disciplines like machine learn-ing (ML), paved the way for a wide range of real-world applications in sectors such as visual recognition systems, language understanding, automated translation techniques, and robotic innovations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In recent years, the most prominent sub-field of machine learning (ML), Artificial Neural Networks (ANNs), have been widely and effectively utilized to transform technological advancements and elevate both businesses and daily life towards a new stage of AI sophistication [19][20][21][22][23][24][25].…”

Section: Generalmentioning

confidence: 99%

Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner

Michailidis

Gkelios

et al. 2023

ICA

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Distributed Machine learning has delivered considerable advances in training neural networks by leveraging parallel processing, scalability, and fault tolerance to accelerate the process and improve model performance. However, training of large-size models has exhibited numerous challenges, due to the gradient dependence that conventional approaches integrate. To improve the training efficiency of such models, gradient-free distributed methodologies have emerged fostering the gradient-independent parallel processing and efficient utilization of resources across multiple devices or nodes. However, such approaches, are usually restricted to specific applications, due to their conceptual limitations: computational and communicational requirements between partitions, limited partitioning solely into layers, limited sequential learning between the different layers, as well as training a potential model in solely synchronous mode. In this paper, we propose and evaluate, the Neuro-Distributed Cognitive Adaptive Optimization (ND-CAO) methodology, a novel gradient-free algorithm that enables the efficient distributed training of arbitrary types of neural networks, in both synchronous and asynchronous manner. Contrary to the majority of existing methodologies, ND-CAO is applicable to any possible splitting of a potential neural network, into blocks (partitions), with each of the blocks allowed to update its parameters fully asynchronously and independently of the rest of the blocks. Most importantly, no data exchange is required between the different blocks during training with the only information each block requires is the global performance of the model. Convergence of ND-CAO is mathematically established for generic neural network architectures, independently of the particular choices made, while four comprehensive experimental cases, considering different model architectures and image classification tasks, validate the algorithms’ robustness and effectiveness in both synchronous and asynchronous training modes. Moreover, by conducting a thorough comparison between synchronous and asynchronous ND-CAO training, the algorithm is identified as an efficient scheme to train neural networks in a novel gradient-independent, distributed, and asynchronous manner, delivering similar – or even improved results in Loss and Accuracy measures.

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