Grarep
This is a python implementation of the paper "GraRep: Learning Graph Representations with Global Structural Information". The improved implementation takes isolated nodes into consideration, generates distributed representation using Autoencoder instead of factorising matrix with SVD.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Dimensionality_reduction_alo_codes | 1,949 | a year ago | 4 | apache-2.0 | Python | |||||
| 特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现 | ||||||||||
| Pca Svd Autoencoder Fourier Wavelet Transformation For Denoising | 15 | 2 years ago | mit | Python | ||||||
| PCA-SVD-Autoencoder-Fourier-Wavelet-Transformation-for-denoising | ||||||||||
| Physicsawareae | 13 | 2 years ago | Jupyter Notebook | |||||||
| The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized. | ||||||||||
| Ml Image Denoising | 12 | 7 years ago | Jupyter Notebook | |||||||
| Image denoising using PCA, NMF, K-SVD, Spectral decomposition, CNN and state of the art generative adversarial denoising autoencoder | ||||||||||
| Photowct | 9 | a year ago | Python | |||||||
| Unofficial implementation of "A Closed-form Solution to Photorealistic Image Stylization" | ||||||||||
| Neural Nets Compression | 7 | 7 years ago | Java | |||||||
| Neural Networks Compression (Multi-Layer Perceptron, Softmax, Autoencoder) by SVD, Sparsification, KMeans quantization and Fourier transformation | ||||||||||
| Grarep | 6 | 7 years ago | Python | |||||||
| This is a python implementation of the paper "GraRep: Learning Graph Representations with Global Structural Information". The improved implementation takes isolated nodes into consideration, generates distributed representation using Autoencoder instead of factorising matrix with SVD. | ||||||||||
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