High Dimensionality What are some technical approaches to improve the interpretability of neural networks

... informational methods. The role of informational methods is to model ... Mechanical modeling; neural network; column-base connection; composed modeling ... Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as ... We do so by performing experiments on artificial neural networks in the context of fairness in machine learning, where the goal is to induce fairness in the ... In this work we study the distributed representations learnt by generative neural network models. In particular, we investigate the properties of redundant and ... Share your videos with friends, family, and the world. ... information is transformed into valuable information with neural net. The individual element of the neural network, i.e., the living neuronal cell, is ... ... information-processing system in order to have a mind. The answer is the ... neural network (see Shea, 2007). Nevertheless, we can ignore this set of ... May 1, 2020 ... This review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals. Mar 24, 2024 ... Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical ... Data-driven network performance prediction for B5G networks: a graph neural network approach. Yaqoob, M., Trestian, R. and Nguyen, H. 2022. Data-driven ...