XeniaLLL

PPGenCDR

This repository is the implementation of AAAI 2023: PPGenCDR

HyperFed_and_FedRANE-master

FedU2

This repository is an official PyTorch implementation of paper: Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data. CVPR 2024.

Autoformer

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

continuous-latent-process-flows

Code, data, and pre-trained models for the paper "Continuous Latent Process Flows" (NeurIPS 2021)

cornac

A Comparative Framework for Multimodal Recommender Systems

critical_look_causal_dlvms

CSDI

Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"

EGI

Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization (NeurIPS 21')

FedAttack

Source code of FedAttack.

Few_Shot_Distribution_Calibration

[ICLR2021 Oral] Free Lunch for Few-Shot Learning: Distribution Calibration

GDN

Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series"

glow-pytorch

PyTorch implementation of Glow

GNN-LM

HierST

The open-source implementation of the paper "HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting"

HIST

The source code and data of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information".

IAPTT-GM

Code Repository for NeurIPS 2021 accepted paper, named "Torwards Gradient-based Bilevel Optimization with non-convex Followers and Beyond"

L-MARVEL

M2m

Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

neural-function-distributions

Pytorch implementation of Generative Models as Distributions of Functions 🌿

OcCo

[ICCV' 21] "Unsupervised Point Cloud Pre-training via Occlusion Completion"

Prior-LT

qlib

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.

Shift-Robust-GNNs

"Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data" (NeurIPS 21')

Stark

[ICCV'21] Learning Spatio-Temporal Transformer for Visual Tracking

StemGNN

Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Temporal_Relational_Stock_Ranking

Code for paper "Temporal Relational Ranking for Stock Prediction"

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

xERM

[AAAI 2022] This is a Pytorch implementation of the AAAI 2022 paper "Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification"