54 citations · 87 across the 20 of their papers we have counts for
5 papers · 1 filter
Diffusion Models With Learned Adaptive Noise
Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa +1
Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images. Central to these algorithms is the diffusion process, a set of equations which ma…
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images
Aaron Gokaslan, A. Feder Cooper, Jasmine Collins +6
We assemble a dataset of Creative-Commons-licensed (CC) images, which we use to train a set of open diffusion models that are qualitatively competitive with Stable Diffusion 2 (SD2…
Text Embeddings Reveal (Almost) As Much As Text
John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov +1
How much private information do text embeddings reveal about the original text? We investigate the problem of embedding \textit{inversion}, reconstructing the full text represented…
Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Jacqueline Maasch, Weishen Pan, Shantanu Gupta +3
Causal discovery is crucial for causal inference in observational studies, as it can enable the identification of valid adjustment sets (VAS) for unbiased effect estimation. Howeve…
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
Junjie Yin, Jiahao Dong, Yingheng Wang +2
We propose a memory-efficient finetuning algorithm for large language models (LLMs) that supports finetuning LLMs with 65B parameters in 2/3/4-bit precision on as little as one 24G…