15 citations · 29 across the 41 of their papers we have counts for
8 papers · 1 filter
Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates
Clement Ruah, Osvaldo Simeone, Jakob Hoydis +1
Embodying the principle of simulation intelligence, digital twin (DT) systems construct and maintain a high-fidelity virtual model of a physical system. This paper focuses on ray t…
Optimizing Information Freshness over a Channel that Wears Out
George J. Stamatakis, Osvaldo Simeone, Nikolaos Pappas
A sensor samples and transmits status updates to a destination through a wireless channel that wears out over time and with every use. At each time slot, the sensor can decide to s…
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models
Matteo Zecchin, Kai Yu, Osvaldo Simeone
Large pre-trained sequence models, such as transformer-based architectures, have been recently shown to have the capacity to carry out in-context learning (ICL). In ICL, a decision…
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory
Haifeng Wen, Hong Xing, Osvaldo Simeone
Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading…
Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone
In many real-world problems, predictions are leveraged to monitor and control cyber-physical systems, demanding guarantees on the satisfaction of reliability and safety requirement…
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks
Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gai…
Towards Efficient and Reliable AI Through Neuromorphic Principles
Bipin Rajendran, Osvaldo Simeone, Bashir M. Al-Hashimi
Artificial intelligence (AI) research today is largely driven by ever-larger neural network models trained on graphics processing units (GPUs). This paradigm has yielded remarkable…
Statistical Complexity of Quantum Learning
Leonardo Banchi, Jason Luke Pereira, Sharu Theresa Jose +1
Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via qu…