8,284 research outputs found
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Many interesting problems in machine learning are being revisited with new
deep learning tools. For graph-based semisupervised learning, a recent
important development is graph convolutional networks (GCNs), which nicely
integrate local vertex features and graph topology in the convolutional layers.
Although the GCN model compares favorably with other state-of-the-art methods,
its mechanisms are not clear and it still requires a considerable amount of
labeled data for validation and model selection. In this paper, we develop
deeper insights into the GCN model and address its fundamental limits. First,
we show that the graph convolution of the GCN model is actually a special form
of Laplacian smoothing, which is the key reason why GCNs work, but it also
brings potential concerns of over-smoothing with many convolutional layers.
Second, to overcome the limits of the GCN model with shallow architectures, we
propose both co-training and self-training approaches to train GCNs. Our
approaches significantly improve GCNs in learning with very few labels, and
exempt them from requiring additional labels for validation. Extensive
experiments on benchmarks have verified our theory and proposals.Comment: AAAI-2018 Oral Presentatio
Design of Reconfigurable Intelligent Surface-Aided Cross-Media Communications
A novel reconfigurable intelligent surface (RIS)-aided hybrid
reflection/transmitter design is proposed for achieving information exchange in
cross-media communications. In pursuit of the balance between energy efficiency
and low-cost implementations, the cloud-management transmission protocol is
adopted in the integrated multi-media system. Specifically, the messages of
devices using heterogeneous propagation media, are firstly transmitted to the
medium-matched AP, with the aid of the RIS-based dual-hop transmission. After
the operation of intermediate frequency conversion, the access point (AP)
uploads the received signals to the cloud for further demodulating and decoding
process. Based on time division multiple access (TDMA), the cloud is able to
distinguish the downlink data transmitted to different devices and transforms
them into the input of the RIS controller via the dedicated control channel.
Thereby, the RIS can passively reflect the incident carrier back into the
original receiver with the exchanged information during the preallocated slots,
following the idea of an index modulation-based transmitter. Moreover, the
iterative optimization algorithm is utilized for optimizing the RIS phase,
transmit rate and time allocation jointly in the delay-constrained cross-media
communication model. Our simulation results demonstrate that the proposed
RIS-based scheme can improve the end-to-end throughput than that of the
AP-based transmission, the equal time allocation, the random and the discrete
phase adjustment benchmarks
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