[ICASSP-2021] Official implementations of Multi-View Contrastive Learning for Online Knowledge Distillation (MCL-OKD)

[AAAI-2020] Official implementations of HCGNets: Gated Convolutional Networks with Hybrid Connectivity for Image Classification

[AAAI-2022 Oral] Official implementations of MCL: Mutual Contrastive Learning for Visual Representation Learning

[AAAI-2022] Official implementation of Prior Gradient Mask Guided Pruning-aware Fine-tuning

[ICME-2022] Official implementations of Localizing Semantic Patches for Accelerating Image Classification

[ECCV-2022] Official implementation of MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition && Pytorch Implementations of some Self-Knowledge Distillation and data augmentation methods

[TPAMI-2023] Official implementations of L-MCL: Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition

[IJCAI-2021&&TNNLS-2022] Official implementation of Hierarchical Self-supervised Augmented Knowledge Distillation

[CVPR-2022] Official implementations of CIRKD: Cross-Image Relational Knowledge Distillation for Semantic Segmentation and implementations on Cityscapes, ADE20K, COCO-Stuff., Pascal VOC and CamVid.

[CVPR-2024] Official implementations of CLIP-KD: An Empirical Study of CLIP Model Distillation

It is the code for Multi-Granularity Spatiotemporal Fusion Transformer

The main code of our proposed multi-resolution interactive transformer

It is the code for the Graph Interpolation Attention Recursive Network

multi-objective optimization, single-objective optimization and reinforcement learning in the field of ensemble learning and time series prediction.

Dynamic ensemble learning based on RL and multi-objective optimization. Deep reinforcement learning and NSGA2 are combined to realize dynamic ensemble learning.

This folder contains spatiotemporal prediction models implemented in combination with Informer and GAT.

Daily ArXiv Papers.

It is the code for the double sampling Transformer

Code for our CIKM'22 paper Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting.

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

中国科学院大学（计算所）《概率方法与随机图》课程作业与教材

A Library for Advanced Deep Time Series Models.

AKGR: Awesome Knowledge Graph Reasoning is a collection of knowledge graph reasoning works, including papers, codes and datasets

⏰ Collaboratively track deadlines of conferences recommended by CCF (Website, Python Cli, Wechat Applet) / If you find it useful, please star this project, thanks~

[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"

A Fair and Scalable Time Series Forecasting Benchmark and Toolkit.