Macheche's repositories
PFL-Non-IID
The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage of the Non-IID data to learn the personalized model for each user. Thanks to @Stonesjtu, this platform can also record the GPU memory usage for the model.
AWGF
Codes for "Understanding and Accelerating Particle-Based Variational Inference" (ICML-19)
bayes-by-backprop
PyTorch implementation of "Weight Uncertainty in Neural Networks"
few_shot_meta_learning
Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch
GNN_Lp_regularizer_proximal
Stability and Generalization of $\ell_p$-Regularized Stochastic Learning for GCN
scipy2018-jupyterlab-tutorial
Tutorial material and instruction for scipy 2018 jupyterlab tutorial