What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
His primary areas of study are Artificial intelligence, Machine learning, Speech recognition, Convolutional neural network and Pattern recognition.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Natural language processing.
The Machine learning study combines topics in areas such as Test data, Space, Heuristic and Parallel computing.
His Speech recognition study incorporates themes from End-to-end principle, Intelligent word recognition and Multi-task learning.
His study on Convolutional neural network also encompasses disciplines like
- Cluster analysis and related Context and Embedding,
- Document clustering which connect with Deep learning, Dimensionality reduction and Unsupervised learning.
His work deals with themes such as Matching, Recurrent neural network, Pooling and Speech coding, which intersect with Pattern recognition.
His most cited work include:
- Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (504 citations)
- Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition (248 citations)
- Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme (174 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Speech recognition, Natural language processing, Pattern recognition and Artificial neural network.
His research combines Machine learning and Artificial intelligence.
His studies link Mandarin Chinese with Speech recognition.
The concepts of his Natural language processing study are interwoven with issues in Context and Word.
His Pattern recognition research is multidisciplinary, relying on both Normalization, Feature and Feature.
His studies deal with areas such as Language model, Recurrent neural network and Reduction as well as Word error rate.
He most often published in these fields:
- Artificial intelligence (64.16%)
- Speech recognition (49.39%)
- Natural language processing (27.60%)
What were the highlights of his more recent work (between 2018-2021)?
- Speech recognition (49.39%)
- Artificial intelligence (64.16%)
- Pattern recognition (24.70%)
In recent papers he was focusing on the following fields of study:
Speech recognition, Artificial intelligence, Pattern recognition, Encoder and End-to-end principle are his primary areas of study.
His Speech recognition study deals with Decoding methods intersecting with Machine translation.
He combines subjects such as Machine learning and Natural language processing with his study of Artificial intelligence.
He has included themes like Working memory, Semantic memory and Memorization in his Natural language processing study.
The study incorporates disciplines such as Variation, Computation and Multi output in addition to Pattern recognition.
His End-to-end principle research incorporates elements of Multi-task learning, Connectionism and Leverage.
Between 2018 and 2021, his most popular works were:
- Self-attention Aligner: A Latency-control End-to-end Model for ASR Using Self-attention Network and Chunk-hopping (45 citations)
- A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule. (23 citations)
- Adapting Translation Models for Transcript Disfluency Detection (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Bo Xu mostly deals with Artificial intelligence, Speech recognition, Encoder, Pattern recognition and Machine translation.
He interconnects Machine learning and Natural language processing in the investigation of issues within Artificial intelligence.
His Natural language processing research includes elements of Semantic memory, Dialog box and Memorization.
His research in Speech recognition intersects with topics in End-to-end principle, Character, Feature and Reduction.
His work carried out in the field of Pattern recognition brings together such families of science as Pyramid, Feature, Robustness and Spiking neural network.
His studies in Machine translation integrate themes in fields like Decoding methods and Translation.
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