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Found 8 papers, 0 papers with code
Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating
Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNN), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations.
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems
This review monograph provides an introduction to meta-learning by covering principles, algorithms, theory, and engineering applications.
Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines
Near-term noisy intermediate-scale quantum circuits can efficiently implement implicit probabilistic models in discrete spaces, supporting distributions that are practically infeasible to sample from using classical means.
Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
In this work, we study a two-layer hybrid classical-quantum classifier in which a first layer of quantum stochastic neurons implementing generalized linear models (QGLMs) is followed by a second classical combining layer.
Bayesian Active Meta-Learning for Black-Box Optimization
Data-efficient learning algorithms are essential in many practical applications for which data collection is expensive, e. g., for the optimal deployment of wireless systems in unknown propagation scenarios.
Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks
In this paper, we consider the problem of power control for a wireless network with an arbitrarily time-varying topology, including the possible addition or removal of nodes.
Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks
Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interference graphs.
Inference over Wireless IoT Links with Importance-Filtered Updates
We propose a data filtering scheme employed by the IoT nodes, which we refer to as distributed importance filtering in order to filter out redundant data samples already at the IoT nodes.
