NLP

Vision Transformers (ViTs) are redefining image recognition by using Transformer models to capture global context, unlike traditional Convolutional Neural Networks (CNNs) that focus on local features. ViTs excel with large datasets and show impressive scalability and performance.

This blog post explains how self-attention and softmax function in Transformer models, crucial for modern NLP. It breaks down how self-attention helps models understand relationships between tokens and how softmax ensures efficient computation and numerical stability.

Less is More: Parameter-Free Text Classification with Gzip offers a novel text classification method using gzip compression, eliminating manual parameter tuning.