What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
Jaime G. Carbonell mainly focuses on Artificial intelligence, Machine learning, Natural language processing, Information retrieval and Active learning.
His Artificial intelligence research includes elements of Context, Analogy and Pattern recognition.
Jaime G. Carbonell works mostly in the field of Machine learning, limiting it down to topics relating to Sampling and, in certain cases, Range, Reduction and Density estimation, as a part of the same area of interest.
His work on Natural language as part of general Natural language processing research is frequently linked to Tree transducers, thereby connecting diverse disciplines of science.
His Information retrieval study combines topics in areas such as Baseline and Set.
Jaime G. Carbonell focuses mostly in the field of Ranking, narrowing it down to matters related to Sentiment analysis and, in some cases, Language model.
His most cited work include:
- Machine Learning: An Artificial Intelligence Approach (2167 citations)
- XLNet: Generalized Autoregressive Pretraining for Language Understanding (1425 citations)
- The Use of MMR and Diversity-Based Reranking for Reodering Documents and Producing Summaries (1185 citations)
What are the main themes of his work throughout his whole career to date?
Jaime G. Carbonell spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Information retrieval and Machine translation.
The study incorporates disciplines such as Task, Data mining and Pattern recognition in addition to Artificial intelligence.
His Natural language processing research integrates issues from Speech recognition and Word.
The Machine learning study combines topics in areas such as Multi-task learning and Active learning.
His work in Automatic summarization, Relevance, Multi-document summarization, Query expansion and Document retrieval is related to Information retrieval.
His Machine translation research is multidisciplinary, relying on both Translation and Rule-based machine translation.
He most often published in these fields:
- Artificial intelligence (58.19%)
- Natural language processing (26.78%)
- Machine learning (22.16%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (58.19%)
- Natural language processing (26.78%)
- Machine learning (22.16%)
In recent papers he was focusing on the following fields of study:
Jaime G. Carbonell focuses on Artificial intelligence, Natural language processing, Machine learning, Transfer of learning and Cross lingual.
His Artificial intelligence study incorporates themes from Named-entity recognition, Task and Set.
When carried out as part of a general Natural language processing research project, his work on Automatic summarization and Hindi is frequently linked to work in Structure, therefore connecting diverse disciplines of study.
The concepts of his Machine learning study are interwoven with issues in Question answering and Interval estimation.
His Transfer of learning research incorporates themes from Sampling, Active learning and Benchmark.
His Cross lingual research focuses on subjects like Knowledge base, which are linked to Information retrieval, Bridge, Zero and Scripting language.
Between 2017 and 2021, his most popular works were:
- XLNet: Generalized Autoregressive Pretraining for Language Understanding (1425 citations)
- XLNet: Generalized Autoregressive Pretraining for Language Understanding (1165 citations)
- Transformer-XL: Attentive Language Models beyond a Fixed-Length Context. (706 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
His primary scientific interests are in Artificial intelligence, Natural language processing, Language model, Cross lingual and Named-entity recognition.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Task.
The study incorporates disciplines such as Bootstrapping and Autoregressive model in addition to Machine learning.
His Natural language processing research is multidisciplinary, incorporating perspectives in Semi-supervised learning and Component.
His Language model research includes elements of Transformer, Question answering, Dependency, Ranking and Noise reduction.
His research in Cross lingual intersects with topics in Entity linking and Knowledge base.
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