Popular GitHub Repositories for Reservoir 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

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

PyTorch Implementation for Deep Metric Learning Pipelines

A simple and flexible code for Reservoir Computing architectures like Echo State Networks

A comprehensive code for AI & Robotics.

Simple and readable code for training and sampling from diffusion models

A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing 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

Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

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..

The Noise Contrastive Estimation for softmax output written in Pytorch