| d2l-ai/d2l-en |
20,613 |
|
0 |
0 |
about 2 years ago |
2 |
November 13, 2022 |
115 |
other |
Python |
| Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge. |
| cornellius-gp/gpytorch |
3,337 |
|
4 |
79 |
about 2 years ago |
38 |
June 02, 2023 |
343 |
mit |
Python |
| A highly efficient implementation of Gaussian Processes in PyTorch |
| BayesWatch/deep-kernel-transfer |
142 |
|
0 |
0 |
about 4 years ago |
0 |
|
6 |
|
Python |
| Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020) |
| SimonRennotte/Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control |
76 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
mit |
Python |
| Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gym environments |
| tiskw/random-fourier-features |
66 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
Python |
| Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model |
| t-vi/candlegp |
59 |
|
0 |
0 |
about 6 years ago |
0 |
|
0 |
apache-2.0 |
Jupyter Notebook |
| Gaussian Processes in Pytorch |
| swyoon/pytorch-minimal-gaussian-process |
38 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| A minimal implementation of Gaussian process regression in PyTorch |
| anassinator/gp |
14 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
mit |
Python |
| Differentiable Gaussian Process implementation for PyTorch |
| onnela-lab/gptools |
12 |
|
0 |
0 |
over 2 years ago |
0 |
|
4 |
bsd-3-clause |
Python |
| Gaussian processes on graphs and lattices in Stan and pytorch. |
| IssamLaradji/GP_DRF |
11 |
|
0 |
0 |
over 6 years ago |
0 |
|
3 |
|
Python |
| Official code for "Efficient Deep Gaussian Process Models for Variable-Sized Inputs" - accepted in IJCNN2019 |
