Representation learning has become a cornerstone of artificial intelligence, designed to automatically extract low‐dimensional, meaningful features from high‐dimensional, sparse raw data. By drastically reducing the reliance on manual feature engineering, representation learning enhances model performance across a wide range of tasks. The field has evolved significantly over the past decades, transitioning from early linear methods, such as Principal Component Analysis (PCA), to modern deep learning paradigms powered by neural networks, generative adversarial networks (GANs), and pre‐trained models. Although the rapid development of representation learning has significantly promoted the progress of natural language processing (NLP), computer vision, and recommender systems, the general practitioners still have a poor understanding of its historical background, core principles, and wide range of applications. To some extent, this limits the full development of its potential. To this end, this survey aims to provide a comprehensive and easily understandable overview for a wider audience. This survey conducts a systematic literature review to tease out the evolution of representation learning and analyse its core drivers. At the same time, this survey deeply explains the basic principles of representation learning, and introduces its practical application cases in various fields. This survey also points out the main limitations of current models and prospects the future research directions.
Zhiyong Wang, Qiang He, Jun Mou et al.· Expert systems· 0 citations
We consider the energy-constrained task allocation problem in large-scale Aerial Edge Computing (AEC) systems, which encompasses a series of tightly coupled decision-making processes, including which tasks need to be processed by uncrewed aerial vehicles (UAVs), how to allocate these tasks and balance energy across UAVs for delay-sensitive requirements. However, little attention has been devoted to exploring the above coupled decision-making problem in AEC with various resource and energy constraints, which is further complicated by energy dynamics (UAV battery states), task-specific consumption, and allocation-feedback balance. In this paper, we formulate a multi-dimensional joint optimization problem, simultaneously optimizing task allocation and energy rewarding to maximize long-term system rewards while balancing service quality and energy efficiency. To this end, we propose a green aerial edge computing framework where partial UAVs are equipped with energy harvesting modules to collect ambient energy. To circumvent the intractable computational complexity arising from the coupled energy states of massive UAVs, we design a distributed solution method based on the mean field game, which decouples the dense multi-agent interactions into a game between an individual UAV and the aggregate population state, thereby transforming the complex global optimization problem into a set of equivalent scalable subproblems. We develop an optimal energy valuation scheme to guide UAV behavior. Numerical results show that our mechanism can effectively ensure sustainable system operation while maintaining high quality of service for metaverse users, outperforming existing methods in both system sustainability and service responsiveness.
Lianbo Ma, Dingsige Chen, Yuee Zhou et al.· IEEE Transactions on Mobile...· 0 citations
As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service providers (ESPs) to offer computation offloading services to MDs. As such, a CEC resource trading market is essential for efficient interactions between MDs and ESPs. However, jointly determining the offloading ratios, allocating combinatorial computation and communication resources, and designing appropriate pricing strategies in a dynamic market remains a significant challenge. To this end, we propose a truthful online double auction-based resource allocation mechanism for partial computation offloading (TRAPO) that explicitly accounts for the stochastic nature of both MDs and ESPs. TRAPO first leverages spatial diversity to construct a set of bids for each MD by mapping their task requirements into resource demands through considering MDs’ preferences and partial offloading. Next, we match resource-demanding MDs with resource-supplying ESPs based on adaptive valid price thresholds to maximize social welfare, and calculate the payments of MDs and the rewards of ESPs. Theoretical analyses demonstrate that TRAPO satisfies truthfulness, budget balance, individual rationality, and computational tractability. Simulation experiments further verify the effectiveness and efficiency of TRAPO.
Dongkuo Wu, Xingwei Wang, Xueyi Wang et al.· IEEE Transactions on Mobile...· 0 citations