Jingwen Guo's starred repositories
qlib
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
DALLE2-pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
motion-diffusion-model
The official PyTorch implementation of the paper "Human Motion Diffusion Model"
solo-learn
solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
transformer-time-series-prediction
proof of concept for a transformer-based time series prediction model
LLM4Rec-Awesome-Papers
A list of awesome papers and resources of recommender system on large language model (LLM).
MotionBERT
[ICCV 2023] PyTorch Implementation of "MotionBERT: A Unified Perspective on Learning Human Motion Representations"
motion-latent-diffusion
[CVPR 2023] Executing your Commands via Motion Diffusion in Latent Space, a fast and high-quality motion diffusion model
ActionCLIP
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"
pytorch_diffusion
PyTorch reimplementation of Diffusion Models
Nonstationary_Transformers
Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415
WSDM2022-PTUPCDR
This is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.
semi_perso_user_cold_start
Source code from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps" by L. Briand, G. Salha-Galvan, W. Bendada, M. Morlon and V.A. Tran
Awesome-Skeleton-Based-Models
A systematic collection of various skeleton-based models (Datasets, Papers, Codes, Leaderboards).