What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer vision
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Shape analysis.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Iterative reconstruction, Noise, Pixel, Rendering and Benchmark.
His Computer vision research includes themes of Computer graphics and Outlier.
His work in Pattern recognition addresses subjects such as Artificial neural network, which are connected to disciplines such as Feature, Histogram and Deep learning.
His research in the fields of Reduction overlaps with other disciplines such as Redundancy.
His work in Shape analysis tackles topics such as Computer graphics which are related to areas like Theoretical computer science.
His most cited work include:
- Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks (810 citations)
- Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis (319 citations)
- Pattern-aware Deformation Using Sliding Dockers (240 citations)
What are the main themes of his work throughout his whole career to date?
Michael Wand mainly investigates Artificial intelligence, Speech recognition, Algorithm, Computer vision and Computer graphics.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
His work on Feature extraction as part of general Pattern recognition research is often related to Texture synthesis, thus linking different fields of science.
His work in the fields of Algorithm, such as Matching, overlaps with other areas such as Markov random field.
Many of his research projects under Rendering are closely connected to Data structure with Data structure, tying the diverse disciplines of science together.
Artificial neural network connects with themes related to Feature in his study.
He most often published in these fields:
- Artificial intelligence (33.54%)
- Speech recognition (29.81%)
- Algorithm (21.74%)
What were the highlights of his more recent work (between 2016-2021)?
- Speech recognition (29.81%)
- Artificial intelligence (33.54%)
- Artificial neural network (8.07%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Artificial neural network, Training and Phase.
His work deals with themes such as Motion and Word, which intersect with Speech recognition.
His Artificial intelligence study incorporates themes from Machine learning, Inverse problem and Computer vision.
He has researched Artificial neural network in several fields, including Test, Structure, Muscle activity and Algorithm.
He interconnects Sampling, Convolution and Adaptive control in the investigation of issues within Algorithm.
His work carried out in the field of Phase brings together such families of science as Boltzmann distribution, Energy minimization and Molecular dynamics.
Between 2016 and 2021, his most popular works were:
- Biosignal-Based Spoken Communication: A Survey (74 citations)
- Benchmarking non-photorealistic rendering of portraits (14 citations)
- Domain-Adversarial Training for Session Independent EMG-based Speech Recognition. (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Speech recognition, Training, Domain, Adversarial system and Independence.
His Speech recognition research includes elements of Artificial neural network, End-to-end principle and Noise.
In his work, Michael Wand performs multidisciplinary research in Training and Session.
His work deals with themes such as Accuracy improvement and Recurrent neural network, which intersect with Independence.
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