Dispatches from the edge of chaos — on nonlinear dynamics, AI, emergence, and the mathematics of complex systems.
DINO (Self-Distillation with No Labels) and DINOv2 are self-supervised learning methods that train Vision Transformers to learn powerful visual features without any labeled data, producing representations
DeiT (Data-efficient Image Transformers) and MAE (Masked Autoencoders) are two breakthrough approaches to training Vision Transformers effectively—DeiT through advanced training strategies and distillation, MAE through
The Swin Transformer introduces a hierarchical vision Transformer that computes attention within shifted windows, achieving linear computational complexity with respect to image size while building multi-scale
T5 (Text-to-Text Transfer Transformer) and BART (Bidirectional and Auto-Regressive Transformer) are encoder-decoder Transformer models that frame all NLP tasks as text-to-text problems, excelling at tasks requiring
BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa revolutionized NLP by introducing bidirectional pretraining, enabling models to understand context from both directions simultaneously for superior language
LLaMA, Mistral, Gemma, and Qwen represent the leading family of open-weight large language models, all built on the decoder-only Dense Transformer architecture with specific optimizations that
The Diffusion Transformer (DiT) replaces the traditional U-Net backbone in diffusion models with a Transformer architecture, achieving superior image generation quality with better scalability properties. Architecture
The Vision Transformer (ViT) applies the Transformer architecture directly to image recognition by treating an image as a sequence of patches, achieving state-of-the-art results on image
The Dense Transformer is the foundational architecture behind GPT, LLaMA, and most modern large language models. It processes text by attending to all tokens simultaneously, enabling