Vanishing/Exploding Gradients

Apr 8, 2025 ... Neural networks are computational models that learn how to recognize patterns, develop predictions, and process data. By using algorithms, ... Feb 7, 2018 ... ... information than excitatory connections, known to enhance entropy in the network. ... Neural information · Neuronal dynamics · Topological tools ... 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 ... Sep 10, 2019 ... Using a genetic algorithm approach to find optimal neural network structures for model-free policy learning in reinforcement learning; Using ... Aug 30, 2023 ... To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of 'Pong' to ... 01/01/2024 Maryam Shanechi is elevated to IEEE Fellow. 12/11/2023 Our Nature Biomedical Engineering paper on neural network models with dynamical flexible ... The semantic network ROI spanned a large swathe of heteromodal cortex, including most of the aforementioned cortical areas. The neural similarity structure of ... ... neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition. Information integration is a measure, developed by Tononi and co-researchers, of the capacity for dynamic neural networks to be in informational states ... in the upper network level. Moreover, in a slightly different direction, research on multi-task learning neural networks, an approach aiming at internal ...