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Found 8 papers, 2 papers with code
CAMEL2: Enhancing weakly supervised learning for histopathology images by incorporating the significance ratio
Histopathology image analysis plays a crucial role in cancer diagnosis.
Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication
Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks.
Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO
We design a latent ordinary differential equation (ODE)-based network under the variational auto-encoder (VAE) framework to learn the mapping function from the partial uplink channels to the full downlink ones at the BS side.
Deep Learning Based RIS Channel Extrapolation with Element-grouping
Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks.
Ordinary Differential Equation-based CNN for Channel Extrapolation over RIS-assisted Communication
The reconfigurable intelligent surface (RIS) is considered as a promising new technology for reconfiguring wireless communication environments.
Information Theory Information Theory
Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication
In this paper, we add signal processing units for a few antennas at RIS to partially acquire the channels.
Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO
In massive multiple-input multiple-output (MIMO) systems, the large number of antennas would bring a great challenge for the acquisition of the accurate channel state information, especially in the frequency division duplex mode.
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels.
