Our model follows a standard encoder-decoder transformer architecture, but we modified the encoder’s self-attention mechanism to incorporate pairwise m/z differences as an additive bias.

This paper introduces a compact end-to-end multi-branch convolutional neural network (CNN) architecture designed to decode brain signals from diverse modalities. The model integrates designated ...

Our experimental evaluation shows that our implementation achieves better scalability on multi-core CPUs. We also discuss our approach's potential to be used in other implementations of RNN-based ...