A multivariate analysis of electroencephalography activity reveals super-additive enhancements to the neural encoding of audiovisual stimuli, providing new insights into how the brain integrates ...

Here’s how: prior to the transformer, what you had was essentially a set of weighted inputs. You had LSTMs (long short term memory networks) to enhance backpropagation – but there were still some ...

Now that we’re nearing the end of the Super Bowl festivities, it’s time to get serious about the big game on Sunday between the Seahawks and Patriots. We decided to take a close look at each team’s ...

As a work exploring the existing trade-off between accuracy and efficiency in the context of point cloud processing, Point Transformer V3 (PTV3) has made significant advancements in computational ...

This project implements Vision Transformer (ViT) for image classification. Unlike CNNs, ViT splits images into patches and processes them as sequences using transformer architecture. It includes patch ...

Rotary Positional Embedding (RoPE) is a widely used technique in Transformers, influenced by the hyperparameter theta (θ). However, the impact of varying *fixed* theta values, especially the trade-off ...

Abstract: With the integration of graph structure representation and self-attention mechanism, the graph Transformer (GT) demonstrates remarkable effectiveness in hyperspectral image (HSI) ...