We present a novel architecture, BootPIG, that enables personalized image generation without any test-time fine-tuning.

TLDR Learning from human preferences, specifically Reinforcement Learning from Human Feedback (RLHF) has been a key recent component in the development of large language models such as ChatGPT or Llama2. Up until recently, the impact of human feedback training on text-to-image models was much more limited. In this work, Diffusion-DPO,

TLDR We present ConRad, a novel approach for generating a 3D model from a single RGB image. We introduce a 3D representation that allows us to explicitly constrain the appearance of the object using the input image which leads to preservation of depicted details.   Background Over the last few years,

TL;DR: Text-to-image diffusion models are very adept at generating novel images from a text prompt, but current adaptations of these methods to image editing suffer from a lack of consistency and faithfulness to the original image. Many of these discrepancies can be traced to the difficulty of inverting the

AI can learn the language of biology to create artificial proteins across multiple protein types that are functional and unseen in nature.

AI makes breast cancer therapy decisions more accurate, affordable and accessible using visual clues in pathology images invisible to the eye

In this work, we focus on the opportunity to use AI to promote social welfare through the design of optimal tax policies in dynamic economies.