| GPflow/GPflow |
1,783 |
|
17 |
19 |
about 2 years ago |
39 |
August 09, 2023 |
149 |
apache-2.0 |
Python |
| Gaussian processes in TensorFlow |
| nesl/agrobot |
32 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
bsd-3-clause |
Jupyter Notebook |
| Neural-Kalman GNSS/INS Navigation for Precision Agriculture |
| SheffieldML/deepGPy |
29 |
|
0 |
0 |
almost 10 years ago |
0 |
|
5 |
bsd-3-clause |
Jupyter Notebook |
| Deep GPs with GPy |
| jefmenegazzo/Intelligent-Vehicle-Perception-Based-on-Inertial-Sensing-and-Artificial-Intelligence |
18 |
|
0 |
0 |
about 5 years ago |
0 |
|
|
other |
|
| Intelligent Vehicle Perception Based on Inertial Sensing and Artificial Intelligence |
| sinadabiri/Deep-Semi-Supervised-GPS-Transport-Mode |
11 |
|
0 |
0 |
over 6 years ago |
0 |
|
1 |
|
Python |
| data-hunters/metadata-digger |
9 |
|
0 |
0 |
over 5 years ago |
0 |
|
1 |
apache-2.0 |
Scala |
| Big Data tool for metadata extraction (Exif), enrichment (using DeepLearning) and analysis |
| Calonca/saveSession-ARKit-CoreML |
7 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
apache-2.0 |
Swift |
| A project to show the possibility to save and load session in ARkit using CoreML, the end goal is to make a guided tours app |
| vtsuperdarn/DeepPredTEC |
6 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
mit |
Python |
| Deep Learning on Predicting GPS TEC Maps |
| teran/trackkr |
6 |
|
0 |
0 |
over 12 years ago |
0 |
|
7 |
|
JavaScript |
| django based service for locating gps units |
| SourangshuGhosh/Doubly-Stochastic-Deep-Gaussian-Process |
5 |
|
0 |
0 |
over 5 years ago |
0 |
|
1 |
apache-2.0 |
Python |
| Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression. |
