Continual Learning What are the emerging trends and future directions in the applications of neural networks

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. Nov 9, 2018 ... You don't lose a few neural connections and with it 80% of your consciousness. The richness of our stream of experience is stable across small ... Keywords: Feedforward neural network, complexity, information complexity, neural complexity, radial basis functions, RBF networks, learning. 1. Introduction. 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 ... Essentially, the network learns how to learn, and that is a really puzzling result. 30th Conference on Neural Information Processing Systems (NIPS 2016), ... Feb 28, 2024 ... Intellectualization, cybersecurity, biometric image, deepfake, information technology, neural networks, detection system, instrumental platform, ... We propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma ... Apr 17, 2019 ... ... learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are ... Sep 10, 2019 ... Using a genetic algorithm approach to find optimal neural network structures for model-free policy learning in reinforcement learning; Using ... Neural networks, decision trees, simulation and ... The machine learning approach and the Information Virtue – Information Token – Informational.