15 citations · 22 across the 8 of their papers we have counts for
8 papers
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference
Jianwen Xie, Yaxuan Zhu, Yifei Xu +2
We study a normalizing flow in the latent space of a top-down generator model, in which the normalizing flow model plays the role of the informative prior model of the generator. W…
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models
Yue Zhang, Hongliang Fei, Dingcheng Li +2
Prompt learning is a new learning paradigm which reformulates downstream tasks as similar pretraining tasks on pretrained models by leveraging textual prompts. Recent works have de…
Variational Flow Graphical Model
Shaogang Ren, Belhal Karimi, Dingcheng Li +1
This paper introduces a novel approach to embed flow-based models with hierarchical structures. The proposed framework is named Variational Flow Graphical (VFG) Model. VFGs learn t…
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
Kaize Ding, Dingcheng Li, Alexander Hanbo Li +4
Paraphrase generation is a longstanding NLP task that has diverse applications for downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on larg…
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Kaize Ding, Jianling Wang, Jundong Li +2
Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention i…
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation
Haiyan Yin, Dingcheng Li, Xu Li +1
Training generative models that can generate high-quality text with sufficient diversity is an important open problem for Natural Language Generation (NLG) community. Recently, gen…