Ashish Arora

TL;DR: The 6th CHiME Speech Separation and Recognition Challenge (CHiME-6) as mentioned in this paper was the first challenge activity in the community to tackle an unsegmented multispeaker speech recognition scenario with a complete set of reproducible open source baselines.

TL;DR: It is proved that a layer of ReLU gates can be set up to automatically recover the support of the sparse codes when the data generative model is that of “Sparse Coding”/“Dictionary Learning”.

TL;DR: A rigorous analysis of the possibility that dictionary learning could be performed by gradient descent on Autoencoders, which are R → R neural network with a single ReLU activation layer of size h, and creates a proxy for the expected gradient of this loss function which is motivated with high probability arguments, under natural distributional assumptions on the sparse code x∗.

TL;DR: In this article, it was shown that the norm of the expected gradient of the standard squared loss function is asymptotically (in sparse code dimension) negligible for all points in a small neighborhood of the input matrix.