3 accepted papers
Neural networks have been shown to be vulnerable against minor adversarial perturbations of their inputs, especially for high dimensional data under $\ell_\infty$ attacks.To combat this problem, techniques like adversarial training have been employed to obtain models that are robust on the training…
We consider the reconstruction of group-sparse vectors from sign measurements of random projections. In particular, we establish conditions on the number of measurements under which such signal ensembles can be recovered up to a prescribed accuracy. The results rely on a mixed restricted isometry pr…
In this paper, a novel noise-shaping method for Multi-Channel Linear Prediction (MCLP) is presented. Without special consideration, the quantization noise of the prediction error poses a serious problem in multi-channel prediction as each noise component distorts the reconstruction of every channel…