Marc-Andre Schulz

According to our database, Marc-Andre Schulz authored at least 8 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

TLIMB - A Transfer Learning Framework for IMage Analysis of the Brain.

Marc-Andre Schulz, Jan Philipp Albrecht, Alpay Yilmaz, Alexander Koch, Dagmar Kainmüller, Ulf Leser, Kerstin Ritter

Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference, 2024

2023

Identifying confounders in deep-learning-based model predictions using DeepRepViz.

Roshan Prakash Rane, JiHoon Kim, Arjun Umesha, Didem Stark, Marc-André Schulz, Kerstin Ritter

CoRR, 2023

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research.

Fabian Eitel, Marc-André Schulz, Moritz Seiler, Henrik Walter, Kerstin Ritter

CoRR, 2023

2022

Data Augmentation via Partial Nonlinear Registration for Brain-Age Prediction.

Marc-Andre Schulz, Alexander Koch, Vanessa Emanuela Guarino, Dagmar Kainmueller, Kerstin Ritter

Proceedings of the Machine Learning in Clinical Neuroimaging - 5th International Workshop, 2022

2021

Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction.

Marc-Andre Schulz, Bertrand Thirion, Alexandre Gramfort, Gaël Varoquaux, Danilo Bzdok

CoRR, 2021

FIMAP: Feature Importance by Minimal Adversarial Perturbation.

Matt Chapman-Rounds, Umang Bhatt, Erik Pazos, Marc-Andre Schulz, Konstantinos Georgatzis

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2019

Clusters in Explanation Space: Inferring disease subtypes from model explanations.

Marc-Andre Schulz, Matt Chapman-Rounds, Manisha Verma, Danilo Bzdok, Konstantinos Georgatzis

CoRR, 2019

EMAP: Explanation by Minimal Adversarial Perturbation.

Matt Chapman-Rounds, Marc-Andre Schulz, Erik Pazos, Konstantinos Georgatzis

CoRR, 2019