- In
machine learning,
backpropagation is a
gradient estimation method commonly used for
training a
neural network to
compute its
parameter updates. It...
-
Neural backpropagation is the
phenomenon in which,
after the
action potential of a
neuron creates a
voltage spike down the axon (normal propagation),...
-
feedforward multiplication remains the core,
essential for
backpropagation or
backpropagation through time. Thus
neural networks cannot contain feedback...
-
actual target values in a
given dataset. Gradient-based
methods such as
backpropagation are
usually used to
estimate the
parameters of the network. During...
-
Backpropagation through time (BPTT) is a gradient-based
technique for
training certain types of
recurrent neural networks, such as
Elman networks. The...
- co-author of a
highly cited paper published in 1986 that po****rised the
backpropagation algorithm for
training multi-layer
neural networks,
although they were...
- is not
linearly separable.
Modern neural networks are
trained using backpropagation and are
colloquially referred to as "vanilla" networks. MLPs grew out...
- of
backpropagation, such as the 1974
dissertation of Paul Werbos, as they did not know the
earlier publications.
Rumelhart developed backpropagation around...
- Rprop,
short for
resilient backpropagation, is a
learning heuristic for
supervised learning in
feedforward artificial neural networks. This is a first-order...
-
mathematician and
computer scientist known for
creating the
modern version of
backpropagation. He was born in Pori. He
received his MSc in 1970 and
introduced a...
