Search Results for author:
Found 8 papers, 3 papers with code
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking
To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model.
FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity
Federated Learning (FL) is an emerging framework for distributed processing of large data volumes by edge devices subject to limited communication bandwidths, heterogeneity in data distributions and computational resources, as well as privacy considerations.
Analogue Radio Over Fiber for Next-Generation RAN: Challenges and Opportunities
The radio access network (RAN) connects the users to the core networks, where typically digitised radio over fiber (D-RoF) links are employed.
Analogue Radio over Fiber aided Multi-service Communications for High Speed Trains
Hence, in this article, we propose an analogue radio over fiber (A-RoF) aided multi-service network architecture for high-speed trains, in order to enhance the quality of service as well as reduce the cost of the radio access network (RAN).
Fact-Enhanced Synthetic News Generation
The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy.
MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation
The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two.
Social and Information Networks Computers and Society
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News
Social media has greatly enabled people to participate in online activities at an unprecedented rate.
Feature Interaction-aware Graph Neural Networks
Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks.
