pizibing/symmetrydetection
Automatically exported from code.google.com/p/symmetrydetection
Discovered public repositories for pizibing in the GitHub catalog.
Automatically exported from code.google.com/p/symmetrydetection
Automatically exported from code.google.com/p/cfd-with-cuda
Automatically exported from code.google.com/p/taucs-wrapper
Automatically exported from code.google.com/p/noise-removal
Automatically exported from code.google.com/p/gpucloth
Automatically exported from code.google.com/p/topmod
Automatically exported from code.google.com/p/atomcnc
Automatically exported from code.google.com/p/efficient-sparse-voxel-octrees
Automatically exported from code.google.com/p/cs3451-project-5
Automatically exported from code.google.com/p/meshmorpher
Automatically exported from code.google.com/p/kilo
Automatically exported from code.google.com/p/libactp
Automatically exported from code.google.com/p/mill-visu
Automatically exported from code.google.com/p/skeleton-animation
Automatically exported from code.google.com/p/swingengine
Automatically exported from code.google.com/p/bones-animation
Automatically exported from code.google.com/p/particlelevelsetlibrary
A stacked autoencoder is a neural network consisting of multiple layers of sparse autoencoders in which the outputs of each layer is wired to the inputs of the successive layer.
An unsupervised learning algorithm that applies back propagation, setting the target values to be equal to the inputs. Also, we impose a sparsity constraint on the hidden units, then the autoencoder will still discover interesting structure in the data, even if the number of hidden units is large.
vtkcurvaturetensor
The programming assignments of Probabilistic Graphical Models by Daphne Koller on Coursera