We propose a method for editing images from human instructions: given an input image and
a written instruction that tells the model what to do, our model follows these instructions to …

Large language models (LLMs) have demonstrated remarkable capabilities in language
generation, understanding, and few-shot learning in recent years. An extensive body of work …

Motivated by concerns that large-scale diffusion models can produce undesirable output
such as sexually explicit content or copyrighted artistic styles, we study erasure of specific …

Large text-guided diffusion models, such as DALLE-2, are able to generate stunning
photorealistic images given natural language descriptions. While such models are highly …

Model-based reinforcement learning methods often use learning only for the purpose of
estimating an approximate dynamics model, offloading the rest of the decision-making work …

For robots to perform a wide variety of tasks, they require a 3D representation of the world
that is semantically rich, yet compact and efficient for task-driven perception and planning …

We consider the problem of inferring high-dimensional data $ x $ in a model that consists of
a prior $ p (x) $ and an auxiliary differentiable constraint $ c (x, y) $ on $ x $ given some …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Since their introduction, diffusion models have quickly become the prevailing approach to
generative modeling in many domains. They can be interpreted as learning the gradients of …

Large vision-language models, such as CLIP, learn robust representations of text and
images, facilitating advances in many downstream tasks, including zero-shot classification …