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43 search results found

Point Cloud Utils ⭐ 1,109

A Python library for common tasks on 3D point clouds

Littleballoffur ⭐ 651

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Samplenet ⭐ 324

Differentiable Point Cloud Sampling (CVPR 2020 Oral)

⚡️ zeus: Lightning Fast MCMC ⚡️

Learning_to_sample ⭐ 160

A learned sampling approach for point clouds (CVPR 2019)

Paramonte ⭐ 112

ParaMonte: Plain Powerful Parallel Monte Carlo and MCMC Library for Python, MATLAB, Fortran, C++, C.

Graph_sampling ⭐ 89

Graph Sampling is a python package containing various approaches which samples the original graph according to different sample sizes.

Samplics ⭐ 50

Select, weight and analyze complex sample data

Libsampling ⭐ 48

A Library of Scatterplot Sampling

[MLSys 2022] "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling" by Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, Yingyan Lin

This is an implementation of Deutsch and Deutsch, "Latin hypercube sampling with multidimensional uniformity", Journal of Statistical Planning and Inference 142 (2012) , 763-772

pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation

Astartes ⭐ 39

Better Data Splits for Machine Learning

Liestationarykernels ⭐ 36

Supplementary code for the paper "Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces"

A python library for metabolic networks sampling and analysis

Nautilus ⭐ 34

Neural Network-Boosted Importance Nested Sampling for Bayesian Statistics

Sampling nuclear data and uncertainty

[ICLR 2022] Path integral sampler

X-PSI: X-ray Pulse Simulation and Inference

Inverse binomial sampling for efficient log-likelihood estimation of simulator models

Cep Energy Guided Diffusion ⭐ 24

Official codebase for Exact Energy-Guided Diffusion Sampling via Contrastive Energy Prediction （ICML 2023）

Cvae_exploration_planning ⭐ 24

Learning informed sampling distributions and information gains for efficient exploration planning.

🐚 Fibonacci spiral sampling utilities

Odes_for_sdes ⭐ 17

Deterministic particle dynamics for simulating Fokker-Planck probability flows

Imagetriangulation ⭐ 15

A short python script to tessellate any image into a bunch of colored triangles using weighted blue noise sampling.

Stein Variational Samplers ⭐ 14

Python and MATLAB code for Stein Variational sampling methods

Task Aware_sampling ⭐ 13

TVCG 2022: Task-Aware Sampling Layer for Point-Wise Analysis

audio pitch-shifting & re-sampling utility, based on the EMU SP-1200

Thermodynamicanalyticstoolkit ⭐ 12

Sampling-based approach to analyse neural networks using TensorFlow

Language Modeling ⭐ 12

Pipeline for training Language Models using PyTorch.

Genetics ⭐ 11

Genetic Algorithm in Python, which could be used for Sampling, Feature Select, Model Select, etc in Machine Learning

Image Captioning ⭐ 9

TensorFlow implementation of Text-guided Attention for Image Captioning using scheduled sampling as a learning approach. Generates captions to unseen images through an end-to-end process.

Official repository of "NEnv: Neural Environment Maps for Global Illumination"

Evopreprocess ⭐ 7

A Python Toolkit for Data Preprocessing with Evolutionary and Nature-Inspired Algorithms.

Mesh Blue Noise Sampling ⭐ 7

A short implementation of blue noise sampling for triangle meshes

Video Sampler ⭐ 7

Effective frame sampling for ML applications.

Extended Naive Bayes ⭐ 6

[WIP] Extension of sklearn Naive Bayes models that allows sampling and more feature distributions.

Jaxampler ⭐ 6

🐙 An open-source JAX-based statistical sampling toolkit 🧪

A collection of utility functions

(Bo)son (S)ampling (S)imulator basing on the most current works in the field! Yay!

Diverse_sampling ⭐ 5

Official project of DiverseSampling (ACMMM2022 Paper)

Normalizing Flows ⭐ 5

Modern normalizing flows in Python. Simple to use and easily extensible.

S3GRL is a scalable SGRL method for faster link prediction using efficient precomputations.

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