What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Electroencephalography and Data mining.
His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Signal processing.
His Pattern recognition study combines topics from a wide range of disciplines, such as Hilbert–Huang transform and Covariance matrix.
The concepts of his Machine learning study are interwoven with issues in Drug target and Representation.
He interconnects Reduction, Entropy, Fractal, Permutation entropy and Epilepsy in the investigation of issues within Electroencephalography.
His research integrates issues of Sample entropy and Anesthesia in his study of Entropy.
His most cited work include:
- Deep convolutional neural networks on multichannel time series for human activity recognition (497 citations)
- Building text classifiers using positive and unlabeled examples (482 citations)
- Partially Supervised Classification of Text Documents (410 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Electroencephalography, Pattern recognition, Machine learning and Neuroscience.
He frequently studies issues relating to Data mining and Artificial intelligence.
His research in Electroencephalography intersects with topics in Anesthesia, Speech recognition, Audiology and Epilepsy.
Xiaoli Li specializes in Pattern recognition, namely Wavelet.
His study in Machine learning focuses on Semi-supervised learning in particular.
He mostly deals with Stimulation in his studies of Neuroscience.
He most often published in these fields:
- Artificial intelligence (37.34%)
- Electroencephalography (22.45%)
- Pattern recognition (19.13%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (37.34%)
- Machine learning (13.06%)
- Electroencephalography (22.45%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Electroencephalography, Pattern recognition and Deep learning.
His Artificial intelligence study incorporates themes from Task and Natural language processing.
His Natural language processing research includes elements of Frame semantics, Artificial neural network and Representation.
The Leverage, Feature and Transfer of learning research Xiaoli Li does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Association, therefore creating a link between diverse domains of science.
Electroencephalography is a primary field of his research addressed under Neuroscience.
His Pattern recognition research is multidisciplinary, incorporating elements of Matrix decomposition, Non-negative matrix factorization, Feature, Cognitive impairment and Stability.
Between 2019 and 2021, his most popular works were:
- Incremental Factorization of Big Time Series Data with Blind Factor Approximation (24 citations)
- HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems (20 citations)
- Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Xiaoli Li mainly focuses on Artificial intelligence, Machine learning, Deep learning, Pattern recognition and Electroencephalography.
Xiaoli Li has included themes like Task and Natural language processing in his Artificial intelligence study.
His Feature learning study, which is part of a larger body of work in Machine learning, is frequently linked to Heterogeneous network, bridging the gap between disciplines.
In Pattern recognition, he works on issues like Series, which are connected to Coherence, Motor control, Biological system and Oscillation.
His Electroencephalography research is included under the broader classification of Neuroscience.
His Neuroscience research is multidisciplinary, incorporating perspectives in Non-negative matrix factorization and Mutual information.
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.
