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.
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.
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.
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.
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.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
Chuan Li;Michael Wand.
european conference on computer vision (2016)
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
Chuan Li;Michael Wand.
computer vision and pattern recognition (2016)
Interactive rendering of large volume data sets
S. Guthe;M. Wand;J. Gonser;W. Strasser.
ieee visualization (2002)
Pattern-aware Deformation Using Sliding Dockers
Martin Bokeloh;Michael Wand;Vladlen Koltun;Hans-Peter Seidel.
international conference on computer graphics and interactive techniques (2011)
Symmetry in 3D Geometry: Extraction and Applications
Niloy J. Mitra;Mark Pauly;Michael Wand;Duygu Ceylan.
eurographics (2013)
Structure-aware shape processing
Niloy J. Mitra;Michael Wand;Hao Zhang;Daniel Cohen-Or.
international conference on computer graphics and interactive techniques (2013)
A connection between partial symmetry and inverse procedural modeling
Martin Bokeloh;Michael Wand;Hans-Peter Seidel.
international conference on computer graphics and interactive techniques (2010)
The randomized z-buffer algorithm: interactive rendering of highly complex scenes
Michael Wand;Matthias Fischer;Ingmar Peter;Friedhelm Meyer auf der Heide.
international conference on computer graphics and interactive techniques (2001)
Optimal HDR reconstruction with linear digital cameras
Miguel Granados;Boris Ajdin;Michael Wand;Christian Theobalt.
computer vision and pattern recognition (2010)
Modeling coarticulation in EMG-based continuous speech recognition
Tanja Schultz;Michael Wand.
Speech Communication (2010)
Max Planck Institute for Informatics
University of Bremen
Dalle Molle Institute for Artificial Intelligence Research
University College London
Stanford University
The University of Texas at Austin
École Polytechnique Fédérale de Lausanne
University of Paderborn
University of Bonn
Saarland University
Profile was last updated on December 6th, 2021.
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