Mark Schmidt
Mark Schmidt publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics, on topics such as Machine Learning and Algorithms, Domain Adaptation and Few-Shot Learning and Stochastic Gradient Optimization Techniques.
- World rank
- #187,594 of 1,633,909 ranked researchers
- Rank in Canada
- #6,521 of 38,960
- Works
- 27
- Citations
- 336
- Citations per work
- 12.4
What does Mark Schmidt research?
Shares of their own output, by the field each of their topics belongs to. The grey slice is everything not listed.
- 1Artificial Intelligence48% of their works
- 2Computer Vision and Pattern Recognition18% of their works
- 3Computational Mechanics9% of their works
- 4Molecular Biology7% of their works
- 5Radiology, Nuclear Medicine and Imaging4% of their works
- 6Management Science and Operations Research4% of their works
Research topics
- Machine Learning and Algorithms
- Domain Adaptation and Few-Shot Learning
- Stochastic Gradient Optimization Techniques
- Sparse and Compressive Sensing Techniques
- Neural Networks and Applications
- Gaussian Processes and Bayesian Inference
- Advanced Image and Video Retrieval Techniques
- Advanced Bandit Algorithms Research
Which keywords describe Mark Schmidt's research?
The keywords of their largest research topics: Deep Learning, Convex Optimization, Semi-Supervised Learning, Gaussian Processes, Machine Learning, Active Learning, Image Classification and Neural Networks.
- Variational Inference
- Self-Organizing Maps
- Feature Matching
- Bandit Optimization
- Transfer Learning
- Sparse Representation
- Domain Adaptation
- Neural Networks
- Active Learning
- Gaussian Processes
- Convex Optimization
- Deep Learning
- Semi-Supervised Learning
- Machine Learning
- Image Classification
- Compressed Sensing
- Random Projections
- Stochastic Gradient Descent
- Backpropagation Learning
- Bayesian Optimization
- Local Descriptors
- Sparse Regression
Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most.
All 22 words, with their numbers
- Deep Learning165 topics
- Convex Optimization103 topics
- Semi-Supervised Learning102 topics
- Gaussian Processes82 topics
- Machine Learning82 topics
- Active Learning61 topic
- Image Classification61 topic
- Neural Networks62 topics
- Compressed Sensing41 topic
- Domain Adaptation41 topic
- Random Projections41 topic
- Sparse Representation41 topic
- Stochastic Gradient Descent41 topic
- Transfer Learning41 topic
- Backpropagation Learning21 topic
- Bandit Optimization21 topic
- Bayesian Optimization21 topic
- Feature Matching21 topic
- Local Descriptors21 topic
- Self-Organizing Maps21 topic
- Sparse Regression21 topic
- Variational Inference21 topic
Where does Mark Schmidt work?
Mark Schmidt's main affiliation in the publication record is University of British Columbia, Canada.
How many publications and citations does Mark Schmidt have?
Science Explorer counts 27 works and 336 citations for Mark Schmidt, ranking #187,594 of 1,633,909 researchers worldwide on the composite score.
Ranked on field-normalised excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.
Other researchers named Mark Schmidt
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