Caiming Xiong mainly investigates Artificial intelligence, Natural language processing, Machine learning, Question answering and Artificial neural network. He frequently studies issues relating to State and Artificial intelligence. Caiming Xiong has included themes like Supervised learning, Principle of compositionality and Reinforcement learning in his Natural language processing study.
He has researched Machine learning in several fields, including Parsing, Sentence, Task, Syntax and Automatic summarization. His study looks at the relationship between Question answering and topics such as Context, which overlap with Sentiment analysis, Initialization, Character and Speech recognition. His Artificial neural network research integrates issues from Regularization and Pooling.
His primary areas of study are Artificial intelligence, Natural language processing, Machine learning, Artificial neural network and Language model. His Artificial intelligence research is multidisciplinary, relying on both Context, State and Pattern recognition. Natural language processing and Word are commonly linked in his work.
His Word study combines topics from a wide range of disciplines, such as Speech recognition, Representation and Machine translation. His Machine learning study frequently draws connections to adjacent fields such as Inference. His Question answering research is multidisciplinary, incorporating perspectives in Sentiment analysis and Sentence.
Caiming Xiong mainly focuses on Artificial intelligence, Natural language processing, Machine learning, Language model and Feature learning. His Artificial intelligence study integrates concerns from other disciplines, such as Consistency and Pattern recognition. He combines subjects such as Domain, Latent variable, Logical consequence and Documentation with his study of Natural language processing.
His Machine learning research includes themes of Context and Graph. His studies deal with areas such as Sentence, Selection, Natural language understanding and Transformer as well as Language model. His Feature learning research includes elements of Supervised learning, Unsupervised learning, Mutual information and Cluster analysis.
Caiming Xiong focuses on Artificial intelligence, Natural language processing, Language model, Code and Transformer. His Artificial intelligence study frequently links to other fields, such as Consistency. His study in Natural language processing is interdisciplinary in nature, drawing from both SQL and Robustness.
Caiming Xiong usually deals with Language model and limits it to topics linked to Task oriented and State. The study incorporates disciplines such as Visualization, Interpretability and Human–computer interaction in addition to Transformer. His study looks at the relationship between Artificial neural network and fields such as Dimension, as well as how they intersect with chemical problems.
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A Deep Reinforced Model for Abstractive Summarization
Romain Paulus;Caiming Xiong;Richard Socher.
arXiv: Computation and Language (2017)
Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning
Jiasen Lu;Caiming Xiong;Devi Parikh;Richard Socher.
computer vision and pattern recognition (2017)
Seq2SQL: Generating Structured Queries From Natural Language Using Reinforcement Learning
Victor Zhong;Caiming Xiong;Richard Socher.
arXiv: Computation and Language (2017)
Learned in translation: contextualized word vectors
Bryan McCann;James Bradbury;Caiming Xiong;Richard Socher.
neural information processing systems (2017)
CTRL: A Conditional Transformer Language Model for Controllable Generation
Nitish Shirish Keskar;Bryan McCann;Lav R. Varshney;Caiming Xiong.
arXiv: Computation and Language (2019)
Pointer Sentinel Mixture Models
Stephen Merity;Caiming Xiong;James Bradbury;Richard Socher.
arXiv: Computation and Language (2016)
The Natural Language Decathlon: Multitask Learning as Question Answering
Bryan McCann;Nitish Shirish Keskar;Caiming Xiong;Richard Socher.
arXiv: Computation and Language (2018)
Dynamic Coattention Networks For Question Answering
Caiming Xiong;Victor Zhong;Richard Socher.
international conference on learning representations (2016)
Dynamic Memory Networks for Visual and Textual Question Answering
Caiming Xiong;Stephen Merity;Richard Socher.
arXiv: Neural and Evolutionary Computing (2016)
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Kazuma Hashimoto;Caiming Xiong;Yoshimasa Tsuruoka;Richard Socher.
empirical methods in natural language processing (2017)
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