no code implementations • 7 May 2018 • Sara Bahaadini, Vahid Noroozi, Neda Rohani, Scott Coughlin, Michael Zevin, Aggelos K. Katsaggelos
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks.
no code implementations • 28 Apr 2017 • Sara Bahaadini, Neda Rohani, Scott Coughlin, Michael Zevin, Vicky Kalogera, Aggelos K. Katsaggelos
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO.
no code implementations • 14 Mar 2017 • Nima Dehmamy, Neda Rohani, Aggelos Katsaggelos
We then show that for each layer, the distribution of solutions found by SGD can be estimated using a class-based principal component analysis (PCA) of the layer's input.