Name: Longhui Yu
Type: User
Company: Peking University
Bio: Large Language Model, Continual Learning, Trustworthy AI, Human‑centric/Data‑centric AI, Interactive ML
Twitter: scut_longhui
Location: shenzhen
Blog: https://yulonghui.github.io/
Longhui Yu's Projects
Analysis tools of Machine Learning, Deep Learning, Computer Vision, Continual Learning
Avalanche: an End-to-End Library for Continual Learning.
Awesome things about LLM-powered agents. Papers / Repos / Blogs / ...
long context eval
AI tool to build charts based on text input
Collaborative Tuning of Large Language Models in an Efficient Way
PyTorch implementation of various methods for continual learning (XdG, EWC, online EWC, SI, LwF, GR, GR+distill, RtF, ER, A-GEM, iCaRL).
Evaluate three types of task shifting with popular continual learning algorithms.
Resources for Data Centric AI
A novel zero-shot image harmonization method based on Diffusion Model Prior.
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
Official implementation of "Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving" (DucTeacher). [BMVC 2022]
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
HUG_rebuttal
IELTS word list, for Chinese who wants to take IELTS
Pytorch Imagenet Models Example + Transfer Learning (and fine-tuning)
Tips for Writing a Research Paper using LaTeX
Code for visualizing the loss landscape of neural nets
Approximating neural network loss landscapes in low-dimensional parameter subspaces for PyTorch
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
OpenMMLab Image Classification Toolbox and Benchmark
A pytorch implementation of Maximum Mean Discrepancies(MMD) loss
Official implementation of "Continual Learning by Modeling Intra-Class Variation" (MOCA). [TMLR 2023]
PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722
:star2: Wiki of OI / ICPC for everyone. (某大型游戏线上攻略,内含炫酷算术魔法)
Library for training machine learning models with privacy for training data
:octopus: Guides, papers, lecture, and resources for prompt engineering