We dive into Transformers in Deep Learning, a revolutionary architecture that powers today's cutting-edge models like GPT and ...

This section explores the key components of the Transformer architecture, including input embedding, positional encoding, encoder and decoder layers, and the model training and inference processes.

Bibek Bhattarai details Intel's AMX, highlighting its role in accelerating deep learning on CPUs. He explains how AMX ...

In this paper, we propose a transformer-based MRC-TransUNet framework, which effectively combines the advantages of transformer as well as CNN, reduces the number of parameters of traditional ...

Model for predicting molecular crystal properties is readily adaptable to specific tasks, even with limited data ...