A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI. Every time a human or machine learns how ...

Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material. The ...

Deep neural networks are now pervasive in science and engineering. To train them to perform mathematical functions, such as image recognition, users rely upon a training method known as ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...

Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material. The ...

The approximate logic neuron model (ALNM) is a single neural model with a dynamic dendritic structure. The ALNM uses a neural pruning function to eliminate unnecessary dendrite branches and synapses ...

Training a neural network is the process of finding a set of weight and bias values so that for a given set of inputs, the outputs produced by the neural network are very close to some target values.

A team of researchers from Belgium think that they are close to extending the anticipated end of Moore’s Law, and they didn’t do it with a supercomputer. Using an artificial intelligence (AI) ...

The impressive and rapid development of machine learning (ML) algorithms, especially those that are based on deep neural networks, has made artificial intelligence (AI) a mandatory technology in the ...