An illustrated and intuitive guide on the inner workings of a CNN
In the largest study yet of deep neural networks trained to perform auditory tasks, researchers found most of these models generate internal representations that share properties of representations seen in the human brain when people are listening to the same sounds
So I’ve used neural networks to generate recipes in the past. They’re computer programs that can learn to imitate the data we give them, copying the way that humans drive cars, label images, or translate languages. That is, they try to learn. They’re called “neural” because they have virtual neurons that work a little like the real neurons in our brains. Their virtual brains, however, are really tiny. Where a human has about 86 billion neurons, the neural networks we use today have hundreds to
A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images
Despite their compelling theoretical properties, Bayesian neural networks (BNNs) tend to perform worse than frequentist methods in classification-based uncertainty quantification (UQ) tasks such as out-of-distribution (OOD) detection. In this paper, based on empirical findings in prior works, we hypothesize that this issue is because even recent Bayesian methods have never considered OOD data in their training processes, even though this "OOD training" technique is an integral part of state-of-the-art frequ
Humans can do things that AI cannot do, as we saw earlier, but those abilities are not due to the superior learning ability of a human neuron.
Neural pruning is a necessary process to eliminate underactive neural connections and improve nerve function. Learn about it here
Neural Codes
Coarse-graining microscopic models of biological neural networks to obtain mesoscopic models of neural activities is an essential step towards multi-scale