Zobrazeno 1 - 10
of 20 570
pro vyhledávání: '"A. Simeone"'
Bayesian Neural Networks (BNNs) provide superior estimates of uncertainty by generating an ensemble of predictive distributions. However, inference via ensembling is resource-intensive, requiring additional entropy sources to generate stochasticity w
Externí odkaz:
http://arxiv.org/abs/2411.07902
Inspired by biological processes, neuromorphic computing utilizes spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have
Externí odkaz:
http://arxiv.org/abs/2411.04728
This work investigates a collaborative sensing and data collection system in which multiple unmanned aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing m
Externí odkaz:
http://arxiv.org/abs/2411.02366
Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done without requiring any explicit optimization and by leveraging not only
Externí odkaz:
http://arxiv.org/abs/2410.23882
Radio resource allocation often calls for the optimization of black-box objective functions whose evaluation is expensive in real-world deployments. Conventional optimization methods apply separately to each new system configuration, causing the numb
Externí odkaz:
http://arxiv.org/abs/2410.19837
Autor:
Zhang, Boning, Liu, Dongzhu, Simeone, Osvaldo, Wang, Guanchu, Pezaros, Dimitrios, Zhu, Guangxu
To support real-world decision-making, it is crucial for models to be well-calibrated, i.e., to assign reliable confidence estimates to their predictions. Uncertainty quantification is particularly important in personalized federated learning (PFL),
Externí odkaz:
http://arxiv.org/abs/2410.14390
In modern wireless network architectures, such as Open Radio Access Network (O-RAN), the operation of the radio access network (RAN) is managed by applications, or apps for short, deployed at intelligent controllers. These apps are selected from a gi
Externí odkaz:
http://arxiv.org/abs/2410.00150
Autor:
Zecchin, Matteo, Simeone, Osvaldo
We introduce adaptive learn-then-test (aLTT), an efficient hyperparameter selection procedure that provides finite-sample statistical guarantees on the population risk of AI models. Unlike the existing learn-then-test (LTT) technique, which relies on
Externí odkaz:
http://arxiv.org/abs/2409.15844
Autor:
Zhu, Meiyi, Zecchin, Matteo, Park, Sangwoo, Guo, Caili, Feng, Chunyan, Popovski, Petar, Simeone, Osvaldo
This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication constraints. Targeting multi-label classification problems, such as segment
Externí odkaz:
http://arxiv.org/abs/2409.07902
The information bottleneck (IB) problem is a widely studied framework in machine learning for extracting compressed features that are informative for downstream tasks. However, current approaches to solving the IB problem rely on a heuristic tuning o
Externí odkaz:
http://arxiv.org/abs/2409.07325