Long-Term Dependencies

... neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition. Oct 6, 2022 ... Our results show that synergy increases as neural networks learn multiple diverse tasks. Furthermore, performance in tasks requiring integration ... ... system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an ... Neural networks can realize conditional distributions of the form p(y. z I x. u) specified by network parameter u. The set of such realizable distributions is a ... Sep 27, 2024 ... We exploit large-scale content consumption information from the perspective of heterogeneous networks and learn latent customer representation ... in the upper network level. Moreover, in a slightly different direction, research on multi-task learning neural networks, an approach aiming at internal ... The informational aspects of neural networks performing associative memory following Hebbian learning are analysed in detail. The recall process is decomposed ... 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 ... Informational capacity and recall quality in sparsely encoded Hopfield-like neural network: analytical approaches and computer simulation Jun 29, 2022 ... Neural Networks are one particular type of Machine Learning technique. They are a type of artificial intelligence modeled on the brain.