Biologically plausible learning mechanisms have implications for understanding brain functions and engineering intelligent systems. Inspired by the multi-scale recurrent connectivity in the brain, we ...

In a variety of forms, neural networks have seen an exponential rise in attention during the last decade. Neural networks trained with gradient descent are currently outperforming other, more ...

Artificial Neural Network (ANN) are highly interconnected and highly parallel systems. Back Propagation is a common method of training artificial neural networks so as to minimize objective function.

Quantum error correction algorithms are designed to detect and correct errors in qubits. Due to the fragi ...

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.

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.