2023 - Research.com Rising Star of Science Award
2022 - Research.com Rising Star of Science Award
His primary areas of investigation include Artificial intelligence, Machine learning, Language model, Adversarial system and Natural language processing. His research ties Pattern recognition and Artificial intelligence together. In the field of Machine learning, his study on Leverage overlaps with subjects such as Upper and lower bounds.
His study looks at the intersection of Language model and topics like Commonsense reasoning with Embedding, Matching and Representation. His Adversarial system study also includes fields such as
Zhe Gan mainly focuses on Artificial intelligence, Machine learning, Natural language processing, Language model and Question answering. Zhe Gan frequently studies issues relating to Pattern recognition and Artificial intelligence. His Machine learning study deals with Generative grammar intersecting with Boosting.
His Natural language processing research includes elements of Visualization and Coreference. His Language model study combines topics in areas such as Commonsense reasoning, Inference and Transformer. Zhe Gan has included themes like Matching, Theoretical computer science and Feature learning in his Question answering study.
Zhe Gan spends much of his time researching Artificial intelligence, Language model, Question answering, Transformer and Natural language processing. His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Robustness. While the research belongs to areas of Language model, Zhe Gan spends his time largely on the problem of Inference, intersecting his research to questions surrounding Natural language.
His research in Question answering intersects with topics in Sentence, Theoretical computer science, Feature learning and Closed captioning. His study focuses on the intersection of Transformer and fields such as Information integration with connections in the field of Deep learning and Encoding. Within one scientific family, Zhe Gan focuses on topics pertaining to Coreference under Natural language processing, and may sometimes address concerns connected to Margin and Relation.
His scientific interests lie mostly in Artificial intelligence, Language model, Question answering, Inference and Natural language processing. His Artificial intelligence study frequently links to other fields, such as Machine learning. In his research on the topic of Question answering, Benchmark is strongly related with Feature learning.
His work deals with themes such as Generative grammar, Task analysis and Theoretical computer science, which intersect with Inference. His studies examine the connections between Natural language processing and genetics, as well as such issues in Coreference, with regards to Relation. His work in Commonsense reasoning covers topics such as Embedding which are related to areas like Adversarial system.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Tao Xu;Pengchuan Zhang;Qiuyuan Huang;Han Zhang.
computer vision and pattern recognition (2018)
Variational autoencoder for deep learning of images, labels and captions
Yunchen Pu;Zhe Gan;Ricardo Henao;Xin Yuan.
neural information processing systems (2016)
Patient Knowledge Distillation for BERT Model Compression
Siqi Sun;Yu Cheng;Zhe Gan;Jingjing Liu.
empirical methods in natural language processing (2019)
UNITER: UNiversal Image-TExt Representation Learning
Yen-Chun Chen;Linjie Li;Licheng Yu;Ahmed El Kholy.
european conference on computer vision (2020)
Semantic Compositional Networks for Visual Captioning
Zhe Gan;Chuang Gan;Xiaodong He;Yunchen Pu.
computer vision and pattern recognition (2017)
Adversarial feature matching for text generation
Yizhe Zhang;Zhe Gan;Kai Fan;Zhi Chen.
international conference on machine learning (2017)
StyleNet: Generating Attractive Visual Captions with Styles
Chuang Gan;Zhe Gan;Xiaodong He;Jianfeng Gao.
computer vision and pattern recognition (2017)
FreeLB: Enhanced Adversarial Training for Natural Language Understanding
Chen Zhu;Yu Cheng;Zhe Gan;Siqi Sun.
international conference on learning representations (2020)
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Yizhe Zhang;Michel Galley;Jianfeng Gao;Zhe Gan.
neural information processing systems (2018)
Relation-Aware Graph Attention Network for Visual Question Answering
Linjie Li;Zhe Gan;Yu Cheng;Jingjing Liu.
international conference on computer vision (2019)
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