| elliottwu/unsup3d |
981 |
|
0 |
0 |
almost 5 years ago |
0 |
|
16 |
mit |
Python |
| (CVPR'20 Oral) Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild |
| cgorman/tensorflow-som |
66 |
|
0 |
0 |
almost 4 years ago |
0 |
|
0 |
mit |
Python |
| A multi-gpu implementation of the self-organizing map in TensorFlow |
| neuroailab/LocalAggregation |
53 |
|
0 |
0 |
over 5 years ago |
0 |
|
2 |
|
Python |
| Tensorflow implementation for "Local Aggregation for Unsupervised Learning of Visual Embeddings" |
| tmills/uhhmm |
9 |
|
0 |
0 |
over 8 years ago |
0 |
|
4 |
|
Python |
| Unsupervised HHMM Training |
| rubenandrebarreiro/gpu-cuda-self-organising-maps |
7 |
|
0 |
0 |
about 3 years ago |
0 |
|
0 |
mit |
C++ |
| 🧠 💡 📈 A project based in High Performance Computing. This project was built using CUDA (Compute Unified Device Architecture), C++ (C Plus Plus), C, CMake and JetBrains CLion. The scenario of the project was a GPU-based implementation of the Self-Organising-Maps (S.O.M.) algorithm for Artificial Neural Networks (A.N.N.), with the support of CUDA (Compute Unified Device Architecture), using its offered parallel optimisations and tunings. The final goal of the project was to test the several GPU-based implementations of the algorithm against a given CPU-based implementation of the same algorithm and, evaluate and compare the overall performance (speedup, efficiency and cost). |
