Deep convolutional networks provide state-of-the-art classifications and regressions results
over many high-dimensional problems. We review their architecture, which scatters data …

Endowing machines with sensing capabilities similar to those of humans is a prevalent
quest in engineering and computer science. In the pursuit of making computers sense their …

A scattering transform defines a locally translation invariant representation which is stable to
time-war** deformation. It extends MFCC representations by computing modulation …

This paper presents a new feature extraction algorithm called power normalized Cepstral
coefficients (PNCC) that is motivated by auditory processing. Major new features of PNCC …

A computational model of auditory analysis is described that is inspired by psychoacoustical
and neurophysiological findings in early and central stages of the auditory system. The …

Auditory stimulus reconstruction is a technique that finds the best approximation of the
acoustic stimulus from the population of evoked neural activity. Reconstructing speech from …

Speech comprehension requires that the brain extract semantic meaning from the spectral
features represented at the cochlea. To investigate this process, we performed an fMRI …

Recovering a low-rank tensor from incomplete information is a recurring problem in signal
processing and machine learning. The most popular convex relaxation of this problem …

This work studies the low-rank tensor completion problem, which aims to exactly recover a
low-rank tensor from partially observed entries. Our model is inspired by the recently …

The expression and experience of human behavior are complex and multimodal and
characterized by individual and contextual heterogeneity and variability. Speech and …