Popular GitHub Repositories for Irregular Sampling

LightSeq: A High Performance Library for Sequence Processing and Generation

A procedural Blender pipeline for photorealistic training image generation

Official code for "DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps" (Neurips 2022 Oral)

:dart: Task-oriented embedding tuning for BERT, CLIP, etc.

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

Unofficial implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions". Try at: www.fixmyphoto.ai

Boltzmann Machines in TensorFlow with examples

基于Pytorch和torchtext的自然语言处理深度学习框架。

Pool-based active learning in Python

Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)

Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions' [Liu+, ECCV2018]

PyTorch Implementation for Deep Metric Learning Pipelines

A comprehensive code for AI & Robotics.

Simple and readable code for training and sampling from diffusion models

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

Differentiable Point Cloud Sampling (CVPR 2020 Oral)

Official Repository of the paper "Trajectory Consistency Distillation"

Synthetic Minority Over-Sampling Technique for Regression

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

Awesome Deep Learning for Time-Series Imputation, including an unmissable paper list about applying neural networks to impute incomplete time series containing NaN missing values/data