Bias in Data

Feb 26, 2021 ... We developed a context-aware graph neural network (GNN) model utilizing the pairwise relationship among participants to investigate the ... Sep 14, 2016 ... Feed forward neural networks (FF or FFNN) and perceptrons (P) are very straight forward, they feed information from the front to the back (input ... ... neural networks contribute to familiar face recognition. Original language, English. Article number, 117896. Pages (from-to), 1-15. Number of pages, 15. Journal ... We present a review on the recent advances and emerging opportunities around the theme of analyzing deep neural networks (DNNs) with information-theoretic ... He also gives an example using a neural network class with fixed architecture as the hypothesis class. He is able to predict the qualitative effects of ... Data-driven network performance prediction for B5G networks: a graph neural network approach. Yaqoob, M., Trestian, R. and Nguyen, H. 2022. Data-driven ... ... Learning Capability of Deep Neural Networks: A Positivist Perspective Jaebong Son and Chang Heon Lee. PDF · The Sky Is the Limit: A Review of Drone Research ... Neural Networks, pp. 271–280. RESEARCH IN PROGRESS. • “Seller-Provided Information and Return Policies for Experience Goods,” under review at. Decision ... Graph neural networks (GNNs) have achieved state-of-the- art performance in graph representation learning by exploit- ing statistical correlations. However, ... Keywords: Feedforward neural network, complexity, information complexity, neural complexity, radial basis functions, RBF networks, learning. 1. Introduction.