Unconditional Text Generation
Unconditional Text Generation is a text generation paradigm that produces coherent sequences of natural language without conditioning on any specific input context or prompt, distinguishing itself from conditional approaches by relying solely on internal model dynamics and learned language patterns.
14
Publications
1.4K
Citations
53
Authors
13
Institutions
Publications per year
2017–2026
14
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14
MelNet: A Generative Model for Audio in the Frequency Domain
Sean Vasquez, Mike Lewis · arXiv (Cornell University) · 2019 · 111 citations · Full text
Computing, Linguistics, Acoustics +13
Adversarial Text Generation via Feature-Mover's Distance
Li‐Qun Chen, Shuyang Dai, Chenyang Tao et al. · arXiv (Cornell University) · 2018 · 75 citations · Full text
DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models
Zhengfu He, Tianxiang Sun, Qiong Tang et al. · 2023 · 63 citations · Full text
Topic-Guided Variational Autoencoders for Text Generation
Wenlin Wang, Zhe Gan, Hongteng Xu et al. · arXiv (Cornell University) · 2019 · 57 citations · Full text
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University of California, Davis
Davis, United States
2 h-index
4 publications
80 citations
Microsoft (United States)
Redmond, United States
2 h-index
3 publications
207 citations
Duke University
Durham, United States
2 h-index
11 publications
717 citations
ShangHai JiAi Genetics & IVF Institute
Shanghai, China
1 h-index
1 publications
919 citations
Beijing Academy of Artificial Intelligence
Beijing, China
1 h-index
5 publications
4.6K citations

Buffalo State University
Buffalo, United States
1 h-index
1 publications
57 citations
Baidu (China)
Beijing, China
1 h-index
1 publications
75 citations
Tsinghua University
Beijing, China
1 h-index
6 publications
5.5K citations
1–8 of 13
Repository · 9 publications · 383 citations
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Conference · 2 publications · 40 citations
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Conference · 1 publications · 919 citations
1
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Repository · 1 publications · 10 citations
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