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Found 5 papers, 3 papers with code
Towards Identifiable Unsupervised Domain Translation: A Diversified Distribution Matching Approach
Unsupervised domain translation (UDT) aims to find functions that convert samples from one domain (e. g., sketches) to another domain (e. g., photos) without changing the high-level semantic meaning (also referred to as ``content'').
Quantized Radio Map Estimation Using Tensor and Deep Generative Models
Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting multi-domain (e. g., frequency and space) radio power propagation maps from limited sensor measurements.
Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning
This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI).
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis
Compared to the unquantized version, our empirical study shows that the proposed algorithm enjoys a substantial reduction of communication overheads with virtually no loss in accuracy and convergence speed.
Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models
However, such deep learning (DL)-based SC approaches encounter serious challenges in both off-line model learning (training) and completion (generalization), possibly because the latent state space for generating the radio maps is prohibitively large.
