We present an approach, based on learning intrinsic data manifold, for the initialization of internal state values long short-term memory (LSTM) recurrent neural networks, ensuring consistency with initial observed input data. Exploiting generalized synchronization concept, we argue that converged, “mature” states constitute a function this learned manifold. The dimension manifold then dictates...

Recently, low-dimensional manifold regularization has been recognized as a competitive method for accelerated cardiac MRI, due to its ability capture temporal correlations. However, existing methods have not performed with the nonlinear structure of an underlying manifold. In this paper, we propose deep learning in unrolling manner MRI on Specifically, fixed low-rank tensor (Riemannian) is chos...

Information coming from social media is vital to the understanding of dynamics involved in multiple events such as terrorist attacks and natural disasters. With spread popularization cameras means share content through networks, an event can be followed many different lenses vantage points. However, data present numerous challenges, frequently it necessary a great deal cleaning filtering techni...