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Found 13 papers, 2 papers with code
Is Translation Helpful? An Empirical Analysis of Cross-Lingual Transfer in Low-Resource Dialog Generation
Cross-lingual transfer is important for developing high-quality chatbots in multiple languages due to the strongly imbalanced distribution of language resources.
Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network
The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over the world, especially in some remote areas where vast amounts of informative data are collected by Internet of remote things (IoRT) devices to support various data-driven artificial intelligence (AI) services.
GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques.
TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music
In this paper, we propose TONet, a plug-and-play model that improves both tone and octave perceptions by leveraging a novel input representation and a novel network architecture.
Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community.
3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation
Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysis
EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles
After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas.
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network
Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e. g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes.
Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence
With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications, which are generally resource-hungry and compute-intensive, such that an individual UE can hardly afford by itself in real time.
Knowledge Distillation for Mobile Edge Computation Offloading
In this article, we propose an edge computation offloading framework based on Deep Imitation Learning (DIL) and Knowledge Distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computation tasks online.
HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization.
Distributed, Parallel, and Cluster Computing
NAIRS: A Neural Attentive Interpretable Recommendation System
In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS.
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network.
