Abstract & Overview
Jiwei Li, Thang Luong, Dan Jurafsky. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.
Home/e-Resources/Artificial Intelligence & Machine Learning/A Hierarchical Neural Autoencoder for Paragraphs and Documents
Conference ProceedingsArtificial Intelligence & Machine Learning
A Hierarchical Neural Autoencoder for Paragraphs and Documents
2015
Checking your access to this record.
Cite this record
Jiwei Li, Thang Luong, Dan Jurafsky (2015). A Hierarchical Neural Autoencoder for Paragraphs and Documents. https://doi.org/10.3115/v1/p15-1107Copy in another style
Li, J., Luong, T., & Jurafsky, D. (2015). A Hierarchical Neural Autoencoder for Paragraphs and Documents. https://doi.org/10.3115/v1/p15-1107
Author names are used as deposited, so check their order before you submit.
Metadata is harvested from open-access indexes and can be incomplete. Check a citation against the published version before relying on it — see our open access & attribution policy.
Related Literature
Strategies for Training Large Vocabulary Neural Language Models
Michael Auli, Wenlin Chen
Beyond Hallucination: Generative AI as a Catalyst for Human Creativity and Cognitive Evolution
Weiwei Cai, Ming Gao
In ICCK Transactions on Emerging Topics in Artificial Intelligence
Estimation of soil moisture in paddy field using Artificial Neural Networks
Ryoichi Doi, Chusnul Arif
In INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE
Generative artificial intelligence in the metaverse era
Zhihan Lv
In Cognitive Robotics