Mark Derthick

454 citations
23 papers · 228 indexed · h-index 9

Mark Derthick

21 papers receiving 203 citations

Peers

Mark Derthick
Comparison fields: 5 of 50
  • Computer Vision and Pattern Recognition 131
  • Signal Processing 47
  • Information Systems and Management 27
  • Artificial Intelligence 84
  • Human-Computer Interaction 10
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Shi-Kuo Chang United States
Ed Huai-hsin United States
Zening Qu United States
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Tim Lammarsch Austria
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Countries citing papers authored by Mark Derthick

Since Specialization
Citations

This map shows the geographic impact of Mark Derthick'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 Mark Derthick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Derthick more than expected).

Fields of papers citing papers by Mark Derthick

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Derthick. 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 Mark Derthick. The network helps show where Mark Derthick may publish in the future.

Co-authorship network

The 18 scholars most cited alongside Mark Derthick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mark Derthick Line = papers co-authored together Mark Derthick links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20182
2 20180
3 20180
4 20185
5 20083
6
The Perspectives Browser: Exploratory Data Analysis for Everyone
20051
7 200411
8 20039
9 20016
10 20012
11 200019
12 199750
13
A minimal encoding approach to feature discovery
19919
14
Mundane reasoning by parallel constraint satisfaction
199023
15 199011
16 19904
17
Counterfactual reasoning with direct models
19875
18
Ensemble' Boltzmann Units have Collective Computational Properties like those of Hopfield and Tank Neurons
19872
19
Is Distributed Connectionism Compatible with the Physical Symbol System Hypothesis
198614
20
Variations on the Boltzmann Machine Learning Algorithm
19847

About Mark Derthick

Mark Derthick is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Ecological Modeling, Computer Networks and Communications and Artificial Intelligence, having authored 23 papers that have together received 228 indexed citations. Recurring topics across this work include Data Visualization and Analytics (9 papers), Advanced Database Systems and Queries (6 papers), Data Management and Algorithms (6 papers), Video Analysis and Summarization (4 papers), Neural Networks and Applications (4 papers), Image Retrieval and Classification Techniques (3 papers), Bayesian Modeling and Causal Inference (2 papers) and Logic, Reasoning, and Knowledge (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (131 citations), Signal Processing (47 citations), Information Systems and Management (27 citations), Artificial Intelligence (84 citations) and Human-Computer Interaction (10 citations). Mark Derthick has collaborated with scholars based in United States and France. Frequent co-authors include Steven F. Roth, John Kolojejchick, Geoffrey E. Hinton, David C. Plaut, Alexander G. Hauptmann, Michael G. Christel, Howard D. Wactlar, Andrew W. Moore, John Zimmerman and Tobun D. Ng. Their work appears in journals such as Artificial Intelligence, Bioinformatics, Knowledge-Based Systems, Medical Entomology and Zoology and Research Showcase @ Carnegie Mellon University (Carnegie Mellon University).

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.

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