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Loss meta-learning for forecasting
Meta-learning of loss functions for supervised learning has been used to date for classification tasks, or as a way to enable few-shot learning.
Wireless Coded Caching Can Overcome the Worst-User Bottleneck by Exploiting Finite File Sizes
We address the worst-user bottleneck of wireless coded caching, which is known to severely diminish cache-aided multicasting gains due to the fundamental worst-channel limitation of multicasting transmission.
Information Theory Information Theory
Decentralizing Multi-Operator Cognitive Radio Resource Allocation: An Asymptotic Analysis
We address the problem of resource allocation (RA) for spectrum underlay in a cognitive radio (CR) communication system with multiple secondary operators sharing resource with an incumbent primary operator.
