Dinghan Shen
Dinghan Shen
Verifierad e-postadress på microsoft.com - Startsida
Citeras av
Citeras av
What Makes Good In-Context Examples for GPT-?
J Liu, D Shen, Y Zhang, B Dolan, L Carin, W Chen
arXiv preprint arXiv:2101.06804, 2021
Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation
X Wang, Q Huang, A Celikyilmaz, J Gao, D Shen, YF Wang, WY Wang, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
Joint embedding of words and labels for text classification
G Wang, C Li, W Wang, Y Zhang, D Shen, X Zhang, R Henao, L Carin
arXiv preprint arXiv:1805.04174, 2018
Adversarial feature matching for text generation
Y Zhang, Z Gan, K Fan, Z Chen, R Henao, D Shen, L Carin
International conference on machine learning, 4006-4015, 2017
Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms
D Shen, G Wang, W Wang, MR Min, Q Su, Y Zhang, C Li, R Henao, ...
arXiv preprint arXiv:1805.09843, 2018
Video generation from text
Y Li, M Min, D Shen, D Carlson, L Carin
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
Understanding the solvent-assisted crystallization mechanism inherent in efficient organic–inorganic halide perovskite solar cells
D Shen, X Yu, X Cai, M Peng, Y Ma, X Su, L Xiao, D Zou
Journal of Materials Chemistry A 2 (48), 20454-20461, 2014
Adversarial text generation via feature-mover's distance
L Chen, S Dai, C Tao, H Zhang, Z Gan, D Shen, Y Zhang, G Wang, ...
Advances in neural information processing systems 31, 2018
Topic-guided variational autoencoders for text generation
W Wang, Z Gan, H Xu, R Zhang, G Wang, D Shen, C Chen, L Carin
arXiv preprint arXiv:1903.07137, 2019
A simple but tough-to-beat data augmentation approach for natural language understanding and generation
D Shen, M Zheng, Y Shen, Y Qu, W Chen
arXiv preprint arXiv:2009.13818, 2020
Deconvolutional paragraph representation learning
Y Zhang, D Shen, G Wang, Z Gan, R Henao, L Carin
Advances in Neural Information Processing Systems 30, 2017
Improving sequence-to-sequence learning via optimal transport
L Chen, Y Zhang, R Zhang, C Tao, Z Gan, H Zhang, B Li, D Shen, C Chen, ...
arXiv preprint arXiv:1901.06283, 2019
Improving disentangled text representation learning with information-theoretic guidance
P Cheng, MR Min, D Shen, C Malon, Y Zhang, Y Li, L Carin
arXiv preprint arXiv:2006.00693, 2020
Coda: Contrast-enhanced and diversity-promoting data augmentation for natural language understanding
Y Qu, D Shen, Y Shen, S Sajeev, J Han, W Chen
arXiv preprint arXiv:2010.08670, 2020
Topic compositional neural language model
W Wang, Z Gan, W Wang, D Shen, J Huang, W Ping, S Satheesh, L Carin
International Conference on Artificial Intelligence and Statistics, 356-365, 2018
Deconvolutional latent-variable model for text sequence matching
D Shen, Y Zhang, R Henao, Q Su, L Carin
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
Towards generating long and coherent text with multi-level latent variable models
D Shen, A Celikyilmaz, Y Zhang, L Chen, X Wang, J Gao, L Carin
arXiv preprint arXiv:1902.00154, 2019
Nash: Toward end-to-end neural architecture for generative semantic hashing
D Shen, Q Su, P Chapfuwa, W Wang, G Wang, L Carin, R Henao
arXiv preprint arXiv:1805.05361, 2018
Syntax-infused variational autoencoder for text generation
X Zhang, Y Yang, S Yuan, D Shen, L Carin
arXiv preprint arXiv:1906.02181, 2019
Mixkd: Towards efficient distillation of large-scale language models
KJ Liang, W Hao, D Shen, Y Zhou, W Chen, C Chen, L Carin
arXiv preprint arXiv:2011.00593, 2020
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Artiklar 1–20