Sample Efficiency

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 ... a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process. The recent surge of graph embedding and graph neural networks also call for renewed and novel methodologies for heterogeneous information network analysis. As a ... Oct 6, 2022 ... Our results show that synergy increases as neural networks learn multiple diverse tasks. Furthermore, performance in tasks requiring integration ... Share your videos with friends, family, and the world. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the ... May 29, 2024 ... However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in ... Informational capacity and recall quality in sparsely encoded Hopfield-like neural network: analytical approaches and computer simulation Dec 2, 2021 ... Each phase involves aligning the connections of the neural network with patterns of information contained in the input dataset or preceding ... Jun 23, 2020 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure.