Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Coupled hidden Markov models for complex action recognition
This map shows the geographic impact of Matthew Brand's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew Brand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Brand more than expected).
This network shows the impact of papers produced by Matthew Brand. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew Brand. The network helps show where Matthew Brand may publish in the future.
Co-authorship network of co-authors of Matthew Brand
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Brand.
A scholar is included among the top collaborators of Matthew Brand based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Matthew Brand. Matthew Brand is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Ziming & Matthew Brand. (2017). Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks. Neural Information Processing Systems. 30. 1721–1730.6 indexed citations
Nikolova, Evdokia, Matthew Brand, & David R. Karger. (2006). Optimal route planning under uncertainty. International Conference on Automated Planning and Scheduling. 131–140.63 indexed citations
Nikovski, Daniel & Matthew Brand. (2003). Decision-theoretic group elevator scheduling. International Conference on Automated Planning and Scheduling. 133–142.29 indexed citations
8.
Brand, Matthew & Kun Huang. (2003). A unifying theorem for spectral embedding and clustering. International Conference on Artificial Intelligence and Statistics. 41–48.86 indexed citations
9.
Brand, Matthew. (2003). Continuous nonlinear dimensionality reduction by kernel eigenmaps. International Joint Conference on Artificial Intelligence. 547–552.39 indexed citations
10.
Brand, Matthew. (2002). Charting a Manifold. Neural Information Processing Systems. 15. 985–992.272 indexed citations
11.
Brand, Matthew, et al.. (2002). Subspace Mappings for Image Sequences.3 indexed citations
12.
Brand, Matthew. (2001). Morphable 3D models from video. Computer Vision and Pattern Recognition. 456–463.112 indexed citations
13.
Brand, Matthew. (2000). Finding Variational Structure in Data by Cross-Entropy Optimization. International Conference on Machine Learning. 95–102.2 indexed citations
14.
Brand, Matthew. (1999). Pattern discovery via entropy minimization. International Conference on Artificial Intelligence and Statistics.46 indexed citations
15.
Brand, Matthew. (1998). An Entropic Estimator for Structure Discovery. Neural Information Processing Systems. 11. 723–729.41 indexed citations
16.
Brand, Matthew, et al.. (1998). Voice-Driven Animation.2 indexed citations
17.
Brand, Matthew & Irfan Essa. (1995). Causal Analysis for Visual Gesture Understanding.12 indexed citations
18.
Brand, Matthew, et al.. (1993). Sensible scenes: visual understanding of complex structures through causal analysis. National Conference on Artificial Intelligence. 588–593.12 indexed citations
19.
Brand, Matthew & Lawrence Birnbaum. (1992). Perception as a matter of design. National Conference on Artificial Intelligence.5 indexed citations
20.
Brand, Matthew, et al.. (1992). Seeing is believing: Why vision needs semantics. eScholarship (California Digital Library).5 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.