What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Z. Jane Wang mainly investigates Artificial intelligence, Pattern recognition, Convolutional neural network, Deep learning and Machine learning.
His Artificial intelligence course of study focuses on Computer vision and Independent component analysis.
His Pattern recognition study incorporates themes from Subspace topology, Speech recognition and Electroencephalography.
His Convolutional neural network research incorporates themes from Pooling, Metric, Kernel and Regression.
His Deep learning research is multidisciplinary, incorporating perspectives in Background noise, Epilepsy, Robustness and Benchmark.
His work in the fields of Machine learning, such as Bayesian network, intersects with other areas such as Homogeneous, Group analysis and Network structure.
His most cited work include:
- Image Fusion With Convolutional Sparse Representation (275 citations)
- A CNN Regression Approach for Real-Time 2D/3D Registration (240 citations)
- Deep learning for pixel-level image fusion: Recent advances and future prospects (216 citations)
What are the main themes of his work throughout his whole career to date?
Z. Jane Wang spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Algorithm.
Artificial intelligence is represented through his Deep learning, Convolutional neural network, Feature extraction, Robustness and Image research.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Voxel, Feature and Electroencephalography.
His Electroencephalography study integrates concerns from other disciplines, such as Speech recognition, Electromyography, Parkinson's disease and Blind signal separation.
The various areas that Z. Jane Wang examines in his Algorithm study include Information hiding, MIMO, Communication channel and Digital watermarking.
The study incorporates disciplines such as Watermark, Spread spectrum and Discrete cosine transform in addition to Digital watermarking.
He most often published in these fields:
- Artificial intelligence (58.78%)
- Pattern recognition (35.10%)
- Machine learning (15.92%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (58.78%)
- Pattern recognition (35.10%)
- Algorithm (13.47%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Algorithm, Machine learning and Deep learning.
Z. Jane Wang has included themes like Adaptation and Computer vision in his Artificial intelligence study.
His Computer vision research focuses on Perception and how it relates to Speech recognition.
His research integrates issues of Semantics, Voxel, Blind signal separation and Data set in his study of Pattern recognition.
His Machine learning research integrates issues from Contextual image classification, Feature extraction and Temporal information.
His Deep learning research is multidisciplinary, relying on both Image segmentation and Image retrieval.
Between 2019 and 2021, his most popular works were:
- An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis (10 citations)
- ReMAE: User-Friendly Toolbox for Removing Muscle Artifacts From EEG (9 citations)
- Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Z. Jane Wang mainly investigates Artificial intelligence, Algorithm, Generative model, Communication channel and Feature extraction.
His work deals with themes such as Machine learning, Computer vision and Pattern recognition, which intersect with Artificial intelligence.
Z. Jane Wang has researched Pattern recognition in several fields, including Artificial neural network and MNIST database.
His Algorithm research includes elements of Transfer of learning and Function.
His work in the fields of MIMO overlaps with other areas such as Backscatter and Task analysis.
His work carried out in the field of Feature extraction brings together such families of science as Deep learning and Human–computer interaction.
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