3 citations · 3 across the 5 of their papers we have counts for
6 papers
Covariance-Aware Goodness for Scalable Forward-Forward Learning
Xiaoyi Jiang, Bashir M. Al-Hashimi, Kai Xu
The Forward-Forward algorithm eliminates global gradient flow and full network activations storage. However, in convolutional settings, existing BP-free FF methods significantly un…
How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks
Clement Ruah, Houssem Sifaou, Osvaldo Simeone +1
Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and avai…
Context-Aware Doubly-Robust Semi-Supervised Learning
Clement Ruah, Houssem Sifaou, Osvaldo Simeone +1
The widespread adoption of artificial intelligence (AI) in next-generation communication systems is challenged by the heterogeneity of traffic and network conditions, which call fo…
Sparsity-Aware Optimization of In-Memory Bayesian Binary Neural Network Accelerators
Prabodh Katti, Bashir M. Al-Hashimi, Bipin Rajendran
Bayesian Neural Networks (BNNs) provide principled estimates of model and data uncertainty by encoding parameters as distributions. This makes them key enablers for reliable AI tha…
Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks
Prabodh Katti, Clement Ruah, Osvaldo Simeone +2
Bayesian Neural Networks (BNNs) provide superior estimates of uncertainty by generating an ensemble of predictive distributions. However, inference via ensembling is resource-inten…
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…