# Critical initialisation for deep signal propagation in noisy rectifier neural networks Arnu Pretorius, Elan van Biljon, Steve Kroon, Herman Kamper Stochastic regularisation is an important weapon in the arsenal of a deep learning practitioner. However, despite recent theoretical advances, our understanding of how noise influences signal propagation in deep neural networks remains limited. By extending recent work based on mean field theory, we develop a new framework for signal propagation in stochastic
# Critical initialisation for deep signal propagation in noisy rectifier neural networks Arnu Pretorius, Elan van Biljon, Steve Kroon, Herman Kamper Stochastic regularisation is an important weapon in the arsenal of a deep learning practitioner. However, despite recent theoretical advances, our understanding of how noise influences signal propagation in deep neural networks remains limited. By extending recent work based on mean field theory, we develop a new framework for signal propagation in stochastic
Blog Topics Advertise Join Newsletter 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks While there may always seem to be something new, cool, and shiny in the field of AI/ML, classic statistical methods that leverage machine learning techniques remain powerful and practical for solving many real-world business problems. By Terence Shin , Data Scientist | MSc Analytics & MBA student on March 4, 2022 in Machine Learning --> First, I’m not saying that linear regression is
A team of researchers at RWTH Aachen University's Institute of Information Management in Mechanical Engineering have recently explored the use of neuroscience techniques to determine how information is structured inside artificial ...
Author:: [[P- Brendan Langen]] [[Q- What is the role of AI in facilitating a decentralized discourse graph]] In [[R- Neural Databases]], the authors present a tool, NeuralDB, that requires no schema and is queryable through natural language processing
Even complex things could be explain in a simple way.
The focus of this group are mechanisms that shape the dynamics and information processing in biological and artificial neuronal networks. On the side of biological networks, we are interested in the relationship between the structure and dynamics of neural networks to unveil experimentally testable mechanisms of collective phenomena. For artificial neuronal networks, we develop the physics of AI that allows us to understand and quantify generalization properties and learning. Employing and developing statis
Stochastic Gradient Descent approximates Bayesian sampling
Abstract page for arXiv paper 1707.01429: Theory of the superposition principle for randomized connectionist representations in neural networks