Search Results for: "neural network"
Dynamic Neural networks can be considered as the improvement of the static neural networks by adding more decision algorithms we can make neural networks learning dynamically for the input.
TTFS is a time-coding technique in which neurons’ activity is proportionate to their firing delay.
A probabilistic neural network (PNN) is a sort of feedforward neural network used to handle classification and pattern recognition problems.
Data Scientists at CRED, Ravi Kumar and Samiran Roy explained the essence of using graph neural networks and how the emerging technology is being utilised by CRED.
In the future, we plan to optimise these models further and apply them to new tasks, such as zero-shot learning and self-supervised learning.
Neural network subspaces contain diverse solutions that can be ensembled, approaching the ensemble performance of independently trained networks without the training cost.
dense layer is deeply connected layer from its preceding layer which works for changing the dimension of the output by performing matrix vector multiplication
This post explains most underlined
Plateau phenomenon and it’s remedies
which is related to optimization of ML model.
Neural network pruning, which comprises methodically eliminating parameters from an existing network, is a popular approach for minimizing the resource requirements at test time.
A paper titled “ETA Prediction with Graph Neural Networks in Google Maps” presents a graph neural network estimator for an estimated time of travel (ETA).
Not Quite My Tempo: Why Should We Care About AI Generated Music?
A deep neural network is created by sandwiching “hidden” layers between the input and the output.