Gradient Flow What are dense connections (DenseNets)

In this work we study the distributed representations learnt by generative neural network models. In particular, we investigate the properties of redundant and ... 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 ... Feb 24, 2020 ... We show how to encode both types of domain knowledge into the widely used Gaussian scale mixture priors with Automatic Relevance Determination. ... information transmission by single neurons but not by the entire network. Most surprisingly, addition of a delay can change the dependence of the information ... The semantic network ROI spanned a large swathe of heteromodal cortex, including most of the aforementioned cortical areas. The neural similarity structure of ... Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as ... ... informational methods. The role of informational methods is to model ... Mechanical modeling; neural network; column-base connection; composed modeling ... We present a review on the recent advances and emerging opportunities around the theme of analyzing deep neural networks (DNNs) with information-theoretic ... Aug 3, 2021 ... In our study the hypothesis is tested on data taken from Warsaw Stock Exchange in 2007–2009 years. The hypothesis is tested by predictive ... Apr 8, 2025 ... Neural networks are computational models that learn how to recognize patterns, develop predictions, and process data. By using algorithms, ...