What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
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 most cited work include:
- Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning (713 citations)
- A Deep Reinforced Model for Abstractive Summarization (686 citations)
- Learned in translation: contextualized word vectors (471 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (65.42%)
- Natural language processing (25.42%)
- Machine learning (18.64%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (65.42%)
- Natural language processing (25.42%)
- Machine learning (18.64%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering (87 citations)
- ERASER: A Benchmark to Evaluate Rationalized NLP Models. (78 citations)
- Prototypical Contrastive Learning of Unsupervised Representations (74 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
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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