kaiwxai

kaiwxai's Stars

• thuml/Large-Time-Series-Model

  Official code, datasets and checkpoints for "Timer: Generative Pre-trained Transformers Are Large Time Series Models" (ICML 2024)

  Language:Python21715

• vduong143/CAT-KDD-2024

  Source code for the paper "CAT: Interpretable Concept-based Taylor Additive Models".

  Language:Jupyter Notebook9

• ymcui/Chinese-LLaMA-Alpaca-3

  中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3

  Language:Python1.5k136

• DECO2023/DECO

  Language:Python21

• Lightning-AI/lit-llama

  Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

  Language:Python5.9k516

• meta-llama/llama

  Inference code for Llama models

  Language:Python55.3k9.4k

• ddz16/TSFpaper

  This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.

  1.8k166

• thuml/Time-Series-Library

  A Library for Advanced Deep Time Series Models.

  Language:Python6.1k983

• WxxShirley/Awesome-Graph-Prompt

  Awesome Papers About Performing Prompting On Graphs

  30419

• deepkashiwa20/MemeSTN

  WWW23-Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

  Language:Python151

• LibCity/Bigscity-LibCity

  LibCity: An Open Library for Urban Spatial-temporal Data Mining

  Language:Python885163

• MiaoHaoSunny/MT-ASTN

  Code for Paper "Multi-task Adversarial Spatial-Temporal Networks for Crowd Flow Prediction"

  Language:Python208

• wanzhixiao/MDL

  a pytorch version implement of paper "Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning"

  Language:Jupyter Notebook122

• zhanglijun95/AutoMTL

  Code for "AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning"

  Language:Jupyter Notebook515

• jindongwang/transferlearning

  Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

  Language:Python13.2k3.8k

• Zhang-Zijian/PromptST

  This repo is for the implementation of CIKM 2023 paper: "Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction"

  Language:Python91

• HKUDS/GraphGPT

  [SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"

  Language:Python51839

• Applied-Machine-Learning-Lab/RMTL

  Language:Python214

• Sean-Huang65/AUX-TS

  official code for our KDD21 paper "Adaptive Transfer Learning on Graph Neural Networks"

  Language:Python395

• zhyliu00/TPB

  Code for CIKM'23 "Cross-city Few-shot Traffic Forecasting via Traffic Pattern Bank"

  Language:Python182

• RManLuo/MAMDR

  Official code implementation for ICDE 23 paper MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation

  Language:Python325

• KL4805/CrossTReS

  Repo for CrossTReS: Cross-city Transfer Learning for Traffic Prediction via Source Region Selection

  Language:Python185

• GestaltCogTeam/STEP

  Code for our SIGKDD'22 paper Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting.

  Language:Python32635

• jwwthu/GNN4Traffic

  This is the repository for the collection of Graph Neural Network for Traffic Forecasting.

  1k190

• shengchaochen82/MetePFL

  [IJCAI'23] Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data.

  Language:Python132

• zjukg/KGTransformer

  [Paper][WWW2023] Structure Pre-training and Prompt Tuning for Knowledge Graph Transfer

  Language:Python455

• AndrewZhou924/Awesome-Graph-Prompting

  A curated list of resources for graph prompting methods

  271

• Starlien95/GraphPrompt

  GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks

  Language:Python13313

• MingChen-Sun/GPPT

  Language:Python5313

• sheldonresearch/ProG

  A Unified Python Library for Graph Prompting

  Language:Python48456