Marc-André Schulz - Concepedia

Author

Marc-André Schulz

McGill University ORCID

Also Known As

Marc-Andre Schulz, Marc-André Schulz, Marc‐Andre Schulz, Schulz, Marc-Andre, Schulz, Marc-André

Publications

580

Citations

H-Index

Concepts

All Affiliations

McGill University Mila - Quebec Artificial Intelligence Institute Charité - Universitätsmedizin Berlin RWTH Aachen University Montreal Neurological Institute and Hospital

About

Marc-André Schulz is an author at McGill University specializing in engineering, deep learning, and machine learning.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	4	4	115
2	Social Sciences	3	3	102
3	Data Science	3	3	67
4	Cognitive Science	1	1	48
5	Computer Science	1	1	13

Marc-André Schulz

Publications

488

Citations

H-Index

Page 1

	Year	Citations
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets Marc-André Schulz, B.T. Thomas Yeo, Joshua T Vogelstein, Nature Communications	2020	291
Performance reserves in brain-imaging-based phenotype prediction Marc-André Schulz, Danilo Bzdok, Stefan Haufe, Cell Reports NeuropsychologyEngineeringMachine LearningSocial SciencesNeurology	2023	48
Analysing Humanly Generated Random Number Sequences: A Pattern-Based Approach Marc-André Schulz, Barbara Schmalbach, Peter Brugger, PLoS ONE	2012	40
Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets Marc-André Schulz, B.T. Thomas Yeo, Joshua T Vogelstein, Convolutional Neural NetworkEngineeringMachine LearningLinear ModelsSocial Sciences	2019	35
Inferring disease subtypes from clusters in explanation space Marc-André Schulz, Matt Chapman-Rounds, Manisha Verma, Scientific Reports	2020	29
Performance reserves in brain-imaging-based phenotype prediction Marc-André Schulz, Danilo Bzdok, Stefan Haufe, EngineeringMachine LearningMultimodal LearningSocial SciencesData Science	2022	19
A cognitive fingerprint in human random number generation Marc-André Schulz, Sebastian Baier, Benjamin Timmermann, Scientific Reports	2021	13
FIMAP: Feature Importance by Minimal Adversarial Perturbation Matt Chapman-Rounds, Umang Bhatt, Erik Pazos, Proceedings of the AAAI Conference on Artificial Intelligence Artificial IntelligenceEngineeringMachine LearningNeural NetworkFeature Importance	2021	13

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