Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

随机森林，Random Forest（RF）

SECOND for KITTI/NuScenes object detection

Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

List of papers, code and experiments using deep learning for time series forecasting

Dual-Stage Attention-Based Recurrent Neural Net for Time Series Prediction

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

Rainfall prediction by Convolutional LSTM Encoder-Predictor model in Pytorch

XGBoost for label-imbalanced data: XGBoost with weighted and focal loss functions

Must-read papers on graph neural networks (GNN)

An open-source NLP research library, built on PyTorch.

A clean PointNet++ segmentation model implementation

包括部分数据预处理以及基于Tensorflow的DMVST_Net模型的实现

Representation learning on large graphs using stochastic graph convolutions.

trackintel is a framework for spatio-temporal analysis of movement trajectory and mobility data.

Mapping a variable-length sentence to a fixed-length vector using BERT model

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Using the Pytorch to build an image temporal prediction model of the encoder-forecaster structure, ConvGRU kernel & ConvLSTM kernel

Google AI 2018 BERT pytorch implementation

Identifying Human Mobility via Trajectory Embeddings(TUL)

A voxel based approach for dynamic cluster analysis of molecular dynamics trajectories.

Fatcord's Alternative WaveRNN (Faster training)

spatio-temporal analysis taxis of taxis from Kaggle dataset (https://www.kaggle.com/crailtap/taxi-trajectory/home)

Implementation for the paper "Extrapolating paths with graph neural networks"

This project mainly address the map match for larger low sampling GPS trajectory dataset based on the method proposed and developed by Yang and Gidofalvi(2018)

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

Dynamic Time Warping in Python / C (using ctypes)

《Pytorch模型训练实用教程》中配套代码

A PyTorch implementation of the paper “VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection"