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Found 8 papers, 1 papers with code
Auditing Differential Privacy Guarantees Using Density Estimation
Previous auditing methods tightly capture the privacy guarantees of DP-SGD trained models in the white-box setting where the auditor has access to all intermediate models; however, the success of these methods depends on a priori information about the parametric form of the noise and the subsampling ratio used for sampling the gradients.
Waveform Learning under Phase Noise Impairment for Sub-THz Communications
The large untapped spectrum in sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G.
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss
In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to minimize the number of active antennas in the BS while guaranteeing the per-user throughput requirements.
Blind Coherent Preamble Detection via Neural Networks
Instead, we propose to use NN only for \textit{blind} coherent combining of the signals in the detector to compensate for the channel effect, thus maximize the signal to noise ratio.
Turbo-AI: Iterative Machine Learning Based Channel Estimation for 2D Massive Arrays
Furthermore, we propose an iterative training approach, referred to as Turbo-AI.
Interference Distribution Prediction for Link Adaptation in Ultra-Reliable Low-Latency Communications
Motivated by the fact that most of the URLLC use cases with most extreme latency and reliability requirements are characterized by semi-deterministic traffic, we propose to exploit the time correlation of the interference to compute useful statistics needed to predict the interference power in the next transmission.
Improved Differentially Private Decentralized Source Separation for fMRI Data
In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting.
Distributed Differentially Private Computation of Functions with Correlated Noise
CAPE can be used in conjunction with the functional mechanism for statistical and machine learning optimization problems.
