LeviViana / Torch_sampling
Efficient reservoir sampling implementation for PyTorch
Stars: ✭ 68
Projects that are alternatives of or similar to Torch sampling
Dokai
Collection of Docker images for ML/DL and video processing projects
Stars: ✭ 58 (-14.71%)
Mutual labels: cuda
Gdax Orderbook Ml
Application of machine learning to the Coinbase (GDAX) orderbook
Stars: ✭ 60 (-11.76%)
Mutual labels: cuda
Flattened Cnn
Flattened convolutional neural networks (1D convolution modules for Torch nn)
Stars: ✭ 59 (-13.24%)
Mutual labels: cuda
Pycuda
CUDA integration for Python, plus shiny features
Stars: ✭ 1,112 (+1535.29%)
Mutual labels: cuda
Ggnn
GGNN: State of the Art Graph-based GPU Nearest Neighbor Search
Stars: ✭ 63 (-7.35%)
Mutual labels: cuda
Build Deep Learning Env With Tensorflow Python Opencv
Tutorial on how to build your own research envirorment for Deep Learning with OpenCV, Python, Tensorfow
Stars: ✭ 66 (-2.94%)
Mutual labels: cuda
Minkowskiengine
Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors
Stars: ✭ 1,110 (+1532.35%)
Mutual labels: cuda
Mpm
Simulating on GPU using Material Point Method and rendering.
Stars: ✭ 61 (-10.29%)
Mutual labels: cuda
Tsne Cuda
GPU Accelerated t-SNE for CUDA with Python bindings
Stars: ✭ 1,120 (+1547.06%)
Mutual labels: cuda
Mpn Cov
@ICCV2017: For exploiting second-order statistics, we propose Matrix Power Normalized Covariance pooling (MPN-COV) ConvNets, different from and outperforming those using global average pooling.
Stars: ✭ 63 (-7.35%)
Mutual labels: cuda
Reservoir sampling implementation for Pytorch
Efficient implementation of reservoir sampling for PyTorch.
This implementation complexity is O(min(k, n - k))
.
The main purpose of this repo is to offer a more efficient option
for sampling without replacement than the common workaround
adopted (which is basically permutation followed by indexing).
Installing
git clone https://github.com/LeviViana/torch_sampling
cd torch_sampling
python setup.py build_ext --inplace
Benchmark
Run the Benchmark.ipynb
for details.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].