Institution

Stanford University

A leading research university with major contributions in AI, systems, language, and robotics.

Diffusion Language Models · Stanford University

Diffusion-LM: Controllable Text from Denoising

Diffusion-LM uses continuous denoising over word vectors so gradient guidance can control syntax and other fine-grained attributes without retraining the LM.

Diffusion Models · Stanford University

ControlNet: Adding Spatial Control to Diffusion Models

ControlNet bolts a trainable copy onto a frozen Stable Diffusion via zero-initialized convolutions, so an edge map, depth, pose, or segmentation steers the image — and it trains on under 50k examples.

Alignment · Stanford University

DPO Explained: Aligning LLMs Without the RLHF Reward Model

Direct Preference Optimization solves the RLHF problem with a single classification-style loss on preference pairs — no separate reward model, no RL loop, no sampling during training.

Efficient AI · Stanford University

FlashAttention Explained: IO-Aware Exact Attention, 2-4x Faster

FlashAttention is an exact attention algorithm that uses tiling and recomputation to cut GPU memory traffic, delivering 3x on GPT-2, 15% on BERT-large, and linear memory in sequence length.