Huawei's cloud division said its Pangu large language model achieved a breakthrough in training architecture with a new "Mixture of Group Experts" technology that outperforms competing methods in ...

AI model training is changing, and smaller players are finding ways to compete. But open-source AI alone doesn’t fix the bigger problem I see—centralization.

Despite its computational advantages offered by GPUs in GNN training, the limited GPU memory capacity struggles to accommodate large-scale graph data, making scalability a significant challenge ...

This course provides a hands-on, project-based approach to image classification. Join instructor Terezija Semenski to gain practical experience in preprocessing data, training, and evaluating a ...

It is important to consider that the use of copyrighted content to train an AI model could be infringement (even if the “copying” occurs at an intermediate step, rather than at the output ...

The idea of training-free Graph Neural Networks (TFGNNs) has been presented as a solution to these problems. During transductive node classification, TFGNNs use the concept of “labels as features” ...

This paper presents GReAT (Graph Regularized Adversarial Training), a novel regularization method designed to enhance the robust classification performance of deep learning models. Adversarial ...

In PyTorch's Distributed Data Parallel (DDP), each GPU stores its copy of the model, optimizer, and gradients for its part of the data. Even with just two GPUs, users can see faster training thanks to ...