Algorithmic Trading

Dec 19, 2019 ... Abstract: Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. Feb 26, 2021 ... We developed a context-aware graph neural network (GNN) model utilizing the pairwise relationship among participants to investigate the ... Feb 26, 2024 ... This article introduces a quantitative approach to modeling the cost of control in a neural network architecture when it is required to execute one or more ... Dec 20, 2024 ... In ecosystems, financial markets, and neural networks, entropy fluctuates as new information reshapes the probability landscape. 4.2 The ... same ink serve more than one informational purpose. Using large ... Physics-Informed Neural Networks for Anomaly Detection: A Practitioner's Guide ... 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 ... The brain is a deep and complex recurrent neural network. The models of information processing that have dominated computational neuroscience, by contrast, are ... classification via convolutional deep neural network. In Proceedings of COLING. 2014, the 25th International Conference on Computational Linguistics ... ... information transmission by single neurons but not by the entire network. Most surprisingly, addition of a delay can change the dependence of the information ... ... neural networks contribute to familiar face recognition. Original language, English. Article number, 117896. Pages (from-to), 1-15. Number of pages, 15. Journal ...