A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Semi-decentralized federated learning combines device-to-server and device-to-device communications for faster convergence.
Fog learning distributes ML model training across heterogeneous devices and networks.
Paper introduces uncertainty injection for deep learning robust optimization.
Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources. To deal with the straggler dilemma issue faced in this technique, this paper proposes a new de…
This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that estimates future interference values by filtering measured interference at a given location. The predictor's parameterization is done offline by translating the autocorrelation…
In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point (transmitter location) to any point on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the p…
Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large size problems. Recently different ma…
This paper proposes a method to train multiple neural networks with shared parameters using a reconstruction loss.