Pinned Repositories
GraphsGPT
The official implementation of the ICML'24 paper "A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer".
awesome-graph-transformer
Papers about graph transformers.
Awesome_Graph_Foundation_Models
Accompanied repositories for our paper Graph foundation model
DaizeDong
daizedong.github.io
Daize Dong's personal page.
Introduction-to-Causal-Inference
Notes and slides on Brady Neal's online Causal Inference course in English. 因果推理入门课程(by Brady Neal)的英文笔记，分享以供交流，已完结。
Optimization-Modeling-Algorithm-and-Theory
《最优化：建模、算法与理论》中文版学习笔记，包含原版与注释版pdf教材。
Stanford-CS231n-2021-and-2022
Notes and slides for Stanford CS231n 2021 & 2022 in English. I merged the contents together to get a better version. Assignments are not included. 斯坦福cs231n的课程笔记(英文版本，不含实验代码)，将2021与2022两年的课程进行了合并，分享以供交流。
Unified-MoE-Compression
The official implementation of the paper "Demystifying the Compression of Mixture-of-Experts Through a Unified Framework".
llama-moe
⛷️ LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training
DaizeDong's Repositories
DaizeDong/Unified-MoE-Compression
The official implementation of the paper "Demystifying the Compression of Mixture-of-Experts Through a Unified Framework".
DaizeDong/Stanford-CS231n-2021-and-2022
Notes and slides for Stanford CS231n 2021 & 2022 in English. I merged the contents together to get a better version. Assignments are not included. 斯坦福cs231n的课程笔记(英文版本，不含实验代码)，将2021与2022两年的课程进行了合并，分享以供交流。
DaizeDong/Optimization-Modeling-Algorithm-and-Theory
《最优化：建模、算法与理论》中文版学习笔记，包含原版与注释版pdf教材。
DaizeDong/Introduction-to-Causal-Inference
Notes and slides on Brady Neal's online Causal Inference course in English. 因果推理入门课程(by Brady Neal)的英文笔记，分享以供交流，已完结。
DaizeDong/daizedong.github.io
Daize Dong's personal page.
DaizeDong/awesome-graph-transformer
Papers about graph transformers.
DaizeDong/Awesome_Graph_Foundation_Models
Accompanied repositories for our paper Graph foundation model
DaizeDong/DaizeDong
DaizeDong/HappySnake
DaizeDong/lm-evaluation-harness
A framework for few-shot evaluation of language models.