Implementing a neural network from scratch involves designing its architecture, coding the forward and backward propagat
In recent years, a growing number of computer scientists have tried to develop computational methods inspired by the structure, function and plasticity of neural circuits in the human brain. Achieving a comprehensive understanding of biological neural circuits is of vital importance for the creation of these neuro-inspired computing systems
From image, text to video and audio, GANs are taking the world of data generation to new heights. Uncover the power of Generative Adversarial Networks and future
NeurIPS 2020 MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning Meta Review The paper proposes an approach for incorporating knowledge about symmetries or equivariances into neural network policies by providing a general purpose method for constructing network layers based on knowledge of the relevant transformations. The reviews are generally positive: Identifying effective ways of incorporating prior knowledge of this type into neural networks is an important research challenge that is o
Unveiling the Future of ALS Research: AI's Role in Predicting Neural Network Degeneration The Race Against Time: Unlocking ALS' Secrets with AI Imagine a world where we can predict and potentially halt the progression of a devastating disease like Amyotrophic Lateral Sclerosis (ALS). A groundbreakin
Stochastic gradient descent with a large initial learning rate is widely used for training modern neural net architectures. Although a small initial learning rate allows for faster training and
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Wherein the Infinite-Width Asymptotics of Single-Hidden-Layer Networks Are Shown to Yield Gaussian-Process Limits Under Iid Gaussian Weights, and Kernel/NTK Viewpoints and Implications for Implicit Regularisation Are Surveyed
(2013) Zenke et al. PLoS Computational Biology. Hebbian changes of excitatory synapses are driven by and further enhance correlations between pre- and postsynaptic activities. Hence, Hebbian plasticity forms a positive feedback loop that can lead to instability in simulated neural networks. To ke
Using the IMDB database, an AI expert walks through how to build a neural network with Keras