Parallel design of sparse deep belief network with multi-objective optimization

Li, Yangyang; Fang, Shuangkang; Bai, Xiaoyu; Jiao, Licheng; Marturi, Naresh

doi:10.1016/j.ins.2020.03.084

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…In recommender systems, product-based neural networks (PNNs) are used to learn high-order feature interactions [11], deep neural networks (DNNs) with more than three layers are used to obtain improved generalization capabilities for unseen feature combinations [12], and the wide and deep network aids in modeling low-and highorder feature interactions [13]. A deep belief network (DBN) is generated by stacking multiple restricted Boltzmann machines (RBM) and through the greedy training of the RBM [14,15]. Because a DBN is a generative model based on unlabeled data with a predictive classification function, it is used in recommender systems [16].…”

Section: Introductionmentioning

confidence: 99%

System for Recommending Telecommunication Packages Based on the Deep and Cross Network

Shi

Wang

Wei

et al. 2022

Wireless Communications and Mobile Computing

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With the evolution of the 5th generation mobile network (5G), the telecommunications industry has considerably affected livelihoods and resulted in the development of national economies worldwide. To increase revenue per customer and secure long-term contracts with users, telecommunications firms and enterprises have launched diverse types of telecommunication packages to satisfy varied user requirements. Several systems for recommending telecommunication packages have been recently proposed. However, extracting effective feature information from large and complex consumption data remains challenging. Conventional methods for the recommendation of telecommunications packages either rely on complex expert feature engineering or fail to perform end-to-end deep learning (DL) during training. In this study, we propose a recommender system based on the Deep and Cross Network (DCN), deep belief network (DBN), embedding, and Word2Vec using the learning abilities of DL-based approaches. The proposed system fits the recommender system for telecommunication packages in terms of click-through rate prediction to provide a potential solution to the recommendation challenges faced by telecommunication enterprises. The proposed model captures the finite order interactional and deep hidden features. Additionally, the text information in the data is used to improve the model’s recommendation capability. The proposed method also does not require feature engineering. We conducted comprehensive experiments using real-world datasets, the results of which demonstrated that our proposed method outperformed other methods based on DBNs, DCNs, deep factorization machines, and deep neural networks in terms of the area under the ROC curve, cross entropy (log loss), and recall metrics.

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Section: Introductionmentioning

confidence: 99%

System for Recommending Telecommunication Packages Based on the Deep and Cross Network

Shi

Wang

Wei

et al. 2022

Wireless Communications and Mobile Computing

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“…Deep learning refers to selflearning according to training data without programming every problem to be solved. The goal of the deep learning model is to model the data, to analyze the deep correlation within the data and to establish a knowledge framework [7]. The model is used in prediction, classification, and feature extraction.…”

Section: The Concept Of Smart Finance Integrated With Deep Reinforcement Learningmentioning

confidence: 99%

Application of Deep Reinforcement Learning Algorithm in Smart Finance

Chen

Zhou

2021

Frontiers in Artificial Intelligence and Applications

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Finance is not only the lifeblood of an economy, but also the lever to adjust the macro-economy. A modern economy is a market economy and essentially a developed financial economy. Based on the analysis of the problems faced by traditional finance and the overview of smart finance, this study puts forward the application of deep learning combined with reinforcement learning in smart finance to solve the problems existing in financial activities for the first time, and verifies through experiments. The model has better data and information processing ability compared with the traditional financial analysis mode. It provides higher quality decision-making information and bring more benefits. Taking a bond rating report as an example, it usually takes about 2 hours for manual in-depth analysis and carding, while it only takes about 2 minutes to interpret and refine the report by using the deep reinforcement learning model. The model has a certain reference value to solve the problems of traditional finance.

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“…This motivates a closer look to be taken at new advances in large-scale global optimization, for both single-and multi-objective optimization problems [219]. Given the upsurge of Deep Learning problems in which more than one objective is established [54,57,101,220,221], the use of multi-objective solvers for large-scale optimization seems to be a natural choice.…”

Section: Large-scale Optimization For Model Trainingmentioning

confidence: 99%

Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges

Martinez¹,

Ser²,

Villar-Rodríguez³

et al. 2020

Preprint

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Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyperparametric configurations with improved performance for a given task, to the optimization of the models parameters as a replacement for gradient-based solvers. Indeed, the literature is rich in proposals showcasing the application of assorted nature-inspired approaches for these tasks. In this work we comprehensively review and critically examine contributions made so far based on three axes, each addressing a fundamental question in this research avenue: a) optimization and taxonomy (Why?), including a historical perspective, definitions of optimization problems in Deep Learning, and a taxonomy associated with an in-depth analysis of the literature, b) critical methodological analysis (How?), which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of the literature, and c) challenges and new directions of research (What can be done, and what for?). In summary, three axes -optimization and taxonomy, critical analysis, and challenges -which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.

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Parallel design of sparse deep belief network with multi-objective optimization

Cited by 12 publications

References 32 publications

System for Recommending Telecommunication Packages Based on the Deep and Cross Network

System for Recommending Telecommunication Packages Based on the Deep and Cross Network

Application of Deep Reinforcement Learning Algorithm in Smart Finance

Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges

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