Zhiqing Xiao's starred repositories
awesome-test-time-adaptation
Collection of awesome test-time (domain/batch/instance) adaptation methods
multi-domain-imbalance
[ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond
SubpopBench
[ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
google-research
Google Research
Awesome-LLM4RS-Papers
Large Language Model-enhanced Recommender System Papers
Algorithm-Practice-in-Industry
搜索、推荐、广告、用增等工业界实践文章收集（来源：知乎、Datafuntalk、技术公众号）
KG-LLM-Papers
[Paper List] Papers integrating knowledge graphs (KGs) and large language models (LLMs)
ML-Papers-of-the-Week
🔥Highlighting the top ML papers every week.
Awesome-LLM-Uncertainty-Reliability-Robustness
Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
VAND-APRIL-GAN
[CVPR 2023 Workshop] VAND Challenge: 1st Place on Zero-shot AD and 4th Place on Few-shot AD
Semi-supervised-learning
A Unified Semi-Supervised Learning Codebase (NeurIPS'22)
releasing-research-code
Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)
ConferenceCountdown
Webpage used to countdown time until ML/Compbio conference deadlines
awesome-Vision-and-Language-Pre-training
Recent Advances in Vision and Language Pre-training (VLP)
meta-domain-shift
Experiments on meta-learning algorithms to solve few-shot domain adaptation
Conference-Accepted-Paper-List
Some Conferences' accepted paper lists (including AI, ML, Robotic)
simple-cnaps
Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)
zotero-better-notes
Everything about note management. All in Zotero.
pytorch-meta
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
few_shot_meta_learning
Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch
Auto-PyTorch
Automatic architecture search and hyperparameter optimization for PyTorch
PyTorch-MAML
A PyTorch implementation of Model Agnostic Meta-Learning (MAML) that faithfully reproduces the results from the original paper.