Compressed Representation

In this work we study the distributed representations learnt by generative neural network models. In particular, we investigate the properties of redundant and ... The brain is a deep and complex recurrent neural network. The models of information processing that have dominated computational neuroscience, by contrast, are ... Jan 16, 2021 ... ... informational and non-informational classes, respectively ... The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). 01/01/2024 Maryam Shanechi is elevated to IEEE Fellow. 12/11/2023 Our Nature Biomedical Engineering paper on neural network models with dynamical flexible ... a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process. Sep 11, 2020 ... Alternatively, it is. 10. Page 29. 2.1. Preliminaries on convolutional neural networks often found that adding depth to the network by stacking ... in the upper network level. Moreover, in a slightly different direction, research on multi-task learning neural networks, an approach aiming at internal ... Jun 23, 2020 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure. Sep 22, 2020 ... By recruiting adjacent neurons into oscillations, waves of synchronously firing neuronal networks—and thereby conscious thoughts—can travel ... Sep 27, 2024 ... We exploit large-scale content consumption information from the perspective of heterogeneous networks and learn latent customer representation ...