Energy-based learning algorithms for analog computing: a comparative study
Energy-based learning algorithms have recently gained a surge of interest due to their
compatibility with analog (post-digital) hardware. Existing algorithms include contrastive …
compatibility with analog (post-digital) hardware. Existing algorithms include contrastive …
An Introduction to Bilevel Optimization: Foundations and applications in signal processing and machine learning
Recently, bilevel optimization (BLO) has taken center stage in some very exciting
developments in the area of signal processing (SP) and machine learning (ML). Roughly …
developments in the area of signal processing (SP) and machine learning (ML). Roughly …
Learning with limited samples: Meta-learning and applications to communication systems
Deep learning has achieved remarkable success in many machine learning tasks such as
image classification, speech recognition, and game playing. However, these breakthroughs …
image classification, speech recognition, and game playing. However, these breakthroughs …
Meta-Learning for Wireless Communications: A Survey and a Comparison to GNNs
Deep learning has been used for optimizing a multitude of wireless problems. Yet most
existing works assume that training and test samples are drawn from the same distribution …
existing works assume that training and test samples are drawn from the same distribution …
Flexible training and uploading strategy for asynchronous federated learning in dynamic environments
Federated learning is a fast-developing distributed learning scheme with promising
applications in vertical domains such as industrial automation and connected automated …
applications in vertical domains such as industrial automation and connected automated …
Learning to broadcast for ultra-reliable communication with differential quality of service via the conditional value at risk
Broadcast/multicast communication systems are typically designed to optimize the outage
rate criterion, which neglects the performance of the fraction of clients with the worst channel …
rate criterion, which neglects the performance of the fraction of clients with the worst channel …
Predicting multi-antenna frequency-selective channels via meta-learned linear filters based on long-short term channel decomposition
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels
must operate based on a small number of pilot symbols. This paper proposes novel channel …
must operate based on a small number of pilot symbols. This paper proposes novel channel …
Speeding up Training of Linear Predictors for Multi-Antenna Frequency-Selective Channels via Meta-Learning
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels
must operate based on a small number of pilot symbols. This paper proposes novel channel …
must operate based on a small number of pilot symbols. This paper proposes novel channel …
[PDF][PDF] On Bayesian Methods for Black-Box Optimization: Efficiency, Adaptation and Reliability
Y Zhang, Y Deng - 2024 - kclpure.kcl.ac.uk
Recent advances in many fields ranging from engineering to natural science, require
increasingly complicated optimization tasks in the experiment design, for which the target …
increasingly complicated optimization tasks in the experiment design, for which the target …