- 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),...
-
Backpropagation through time (BPTT) is a gradient-based
technique for
training certain types of
recurrent neural networks, such as
Elman networks. The...
- is not
linearly separable.
Modern neural networks are
trained using backpropagation and are
colloquially referred to as "vanilla" networks. MLPs grew out...
-
feedforward multiplication remains the core,
essential for
backpropagation or
backpropagation through time. Thus
neural networks cannot contain feedback...
-
training neural networks with gradient-based
learning methods and
backpropagation. In such methods,
during each
training iteration, each
neural network...
-
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...
-
Backpropagation through structure (BPTS) is a gradient-based
technique for
training recursive neural networks,
proposed in a 1996
paper written by Christoph...
- like the
standard backpropagation network can
generalize to
unseen inputs, but they are
sensitive to new information.
Backpropagation models can be analogized...
- 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...
