7 accepted papers
State-of-the-art techniques for enhancing robustness of deep networks mostly rely on empirical risk minimization with suitable data augmentation. In this paper, we propose a complementary approach motivated by communication theory, aimed at enhancing the signal-to-noise ratio at the output of a neur…
Speaker recognition, recognizing speaker identities based on voice alone, enables important downstream applications, such as personalization and authentication. Learning speaker representations, in the context of supervised learning, heavily depends on both clean and sufficient labeled data, which i…
Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio…
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up strategy for attenuating adversarial perturbations using a nonlinear front end which polarizes and quantizes the data. We o…
Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fid…
Most prior research in distributed beamforming involves narrowband, frequency nonselective, channels, with the goal of sending a common message from cooperating nodes so that phases of the signals transmitted from the different nodes align at the receiver. The performance metric is the received SNR…
Spectral embedding based on the Singular Value Decomposition (SVD) is a widely used preprocessing step in many learning tasks, typically leading to dimensionality reduction by projecting onto a number of dominant singular vectors and rescaling the coordinate axes (by a predefined function of the sin…