64 citations · 128 across the 5 of their papers we have counts for
5 papers
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Chen Henry Wu, Saman Motamed, Shaunak Srivastava +1
Generative models (e.g., GANs, diffusion models) learn the underlying data distribution in an unsupervised manner. However, many applications of interest require sampling from a pa…
Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance
Chen Henry Wu, Fernando De la Torre
Diffusion models have achieved unprecedented performance in generative modeling. The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually…
Selective Annotation Makes Language Models Better Few-Shot Learners
Hongjin Su, Jungo Kasai, Chen Henry Wu +8
Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they l…
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Hao Zhou, Pei Ke, Zheng Zhang +11
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue dat…
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
Chen Wu, Xuancheng Ren, Fuli Luo +1
Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) th…