Popular GitHub Repositories for Distance 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)

自然语言处理（nlp），小姜机器人（闲聊检索式chatbot），BERT句向量-相似度（Sentence Similarity），XLNET句向量-相似度（text xlnet embedding），文本分类（Text classification）， 实体提取（ner，bert+bilstm+crf），数据增强（text augment, data enhance），同义句同义词生成，句子主干提取（mainpart），中文汉语短文本相似度，文本特征工程，keras-http-service调用

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

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

High-fidelity performance metrics for generative models in PyTorch

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives: https://nvlabs.github.io/instant-ngp/

Boltzmann Machines in TensorFlow with examples

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

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

Simple and readable code for training and sampling from diffusion models

A comprehensive code for AI & Robotics.

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

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"