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Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning
Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis.
SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results.
SemHARQ: Semantic-Aware HARQ for Multi-task Semantic Communications
Intelligent task-oriented semantic communications (SemComs) have witnessed great progress with the development of deep learning (DL).
Semantic Communications with Explicit Semantic Base for Image Transmission
Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications.
Dynamic UAV Swarm Collaboration for Multi-Targets Tracking under Malicious Jamming: Joint Power, Path and Target Association Optimization
First, a cluster-evolutionary target association (CETA) algorithm is proposed, which involves dividing the UAV swarm into the multiple sub-swarms and individually matching these sub-swarms to targets.
Scalable Multi-task Semantic Communication System with Feature Importance Ranking
Semantic communications are expected to be an innovative solution to the emerging intelligent applications in the era of connected intelligence.
Semantic Communication for Internet of Vehicles: A Multi-User Cooperative Approach
Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security.
Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks
In cache-enabling UAV NOMA networks, the caching placement of content caching phase and radio resource allocation of content delivery phase are crucial for network performance.
Cache-enabling UAV Communications: Network Deployment and Resource Allocation
We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS).
Single-Layer Graph Convolutional Networks For Recommendation
The results verify that the proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training, in terms of the recommendation performance.
Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks
Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks.
Policy Gradient from Demonstration and Curiosity
In this work, an integrated policy gradient algorithm was proposed to boost exploration and facilitate intrinsic reward learning from only limited number of demonstrations.
Scalable Learning Paradigms for Data-Driven Wireless Communication
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy.
Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT
The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT.
Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach
Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way.
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions.
Differentially Private ADMM for Distributed Medical Machine Learning
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests.
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine
Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms.
