Popular GitHub Repositories for Sampling Methods

Official implementation for the paper "Model-based Diffusion for Trajectory Optimization". Model-based diffusion (MBD) is a novel diffusion-based trajectory optimization framework that employs a dynamics model to run the reverse denoising process to generate high-quality trajectories.

PyTorch implementation for "Parallel Sampling of Diffusion Models", NeurIPS 2023 Spotlight

Lightweight library of stochastic gradient MCMC algorithms written in JAX.

Official Code for "Non-Probability Sampling Network for Stochastic Human Trajectory Prediction (CVPR 2022)"

Improved sampling via learned diffusions (ICLR2024) and an optimal control perspective on diffusion-based generative modeling (TMLR2024)

A collection of various LLM sampling methods implemented in pure Pytorch

The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural Networks (DNNs), Neural ODEs, and Symplectic Neural Networks (SympNets) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).

This repository provides code for SVD and Importance sampling-based algorithms for large scale topic modeling.

An open-source JAX-based statistical sampling toolkit ?

? Energy-Based Modeling library for PyTorch, offering tools for sampling, inference, and learning in complex distributions.

Machine Learning approaches to perform anomaly detection on industrial data.

PyTorch implementation for " Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference" (https://arxiv.org/abs/1810.02555).

OntoSample is a python package that offers classic sampling techniques for OWL ontologies/knowledge bases. Furthermore, we have tailored the classic sampling techniques to the setting of concept learning making use of learning problem.

The dataset consists of data collected from heavy Scania trucks in everyday usage. The system in focus is the Air Pressure system (APS) which generates pressurised air that is utilized in various functions in a truck, such as braking and gear changes. The dataset’s positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS. So, I created a model where It can able to detect whether the APS is going to fail or Not.

Active Learning for Object Detection

slice sampling with option to add constraints

System for Training-based Expansion of Tools for Proper Name Mentions Recognition Based on Active Learning

Implementation of active learning sampler

BURST: Bayesian Uncertainty-driven Reinforcement Sampling Technique

Implementing inference methods for Latent Dirichlet Allocation model. This repo is for study purposes.