pyprobml Python 3 code for my new book series Probabilistic Machine Learning. This is work in progress, so expect rough edges. Getting less rough… Jupyter notebooks For each chapter there are one or more accompanying Jupyter notebooks that cover some of the material in more detail.When you open a notebook, there will be a button at the top that says ‘Open in colab’. If you click on this, it will start a virtual machine (VM) instance on Google Cloud Platform […]

Graph Contrastive Learning Automated PyTorch implementation for Graph Contrastive Learning Automated . Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang In ICML 2021. Overview In this repository, we propose a principled framework named joint augmentation selection (JOAO), to automatically, adaptively and dynamically select augmentations during GraphCL training.Sanity check shows that the selection aligns with previous “best practices”, as shown in Figure 2. Dependencies Experiments Citation If you use this code for you research, please cite our paper. @article{you2021graph, title={Graph Contrastive […]

SDCLR The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in reality is commonly imbalanced and shows a long-tail distribution, and it is unclear how robustly the latest contrastive learning methods could perform in the practical scenario. This paper proposes to explicitly tackle this challenge, via a principled framework called Self-Damaging Contrastive Learning (SDCLR), to automatically balance the representation learning without knowing the classes. Our main inspiration is […]