Ricards Marcinkevics

According to our database, Ricards Marcinkevics authored at least 15 papers between 2019 and 2024.

Collaborative distances:

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

2024

Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis.

Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt

Medical Image Anal., January, 2024

Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?

Ricards Marcinkevics, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt

CoRR, 2024

2023

Interpretable and explainable machine learning: A methods-centric overview with concrete examples.

Ricards Marcinkevics, Julia E. Vogt

WIREs Data. Mining. Knowl. Discov., 2023

Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss.

Moritz Vandenhirtz, Laura Manduchi, Ricards Marcinkevics, Julia E. Vogt

CoRR, 2023

Breathing New Life into COPD Assessment: Multisensory Home-monitoring for Predicting Severity.

Zixuan Xiao, Michal Muszynski, Ricards Marcinkevics, Lukas Zimmerli, Adam Daniel Ivankay, Dario Kohlbrenner, Manuel Kuhn, Yves Nordmann, Ulrich Muehlner, Christian F. Clarenbach, Julia E. Vogt, Thomas Brunschwiler

Proceedings of the 25th International Conference on Multimodal Interaction, 2023

2022

Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge.

Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt

CoRR, 2022

Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods.

Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt

Proceedings of the Machine Learning for Healthcare Conference, 2022

A Deep Variational Approach to Clustering Survival Data.

Laura Manduchi, Ricards Marcinkevics, Michela Carlotta Massi, Thomas J. Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E. Vogt

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A Deep Variational Approach to Clustering Survival Data.

Laura Manduchi, Ricards Marcinkevics, Michela Carlotta Massi, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Julia E. Vogt

CoRR, 2021

Learning Medical Risk Scores for Pediatric Appendicitis.

Pedro Roig Aparicio, Ricards Marcinkevics, Patricia Reis Wolfertstetter, Sven Wellmann, Christian Knorr, Julia E. Vogt

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

Interpretable Models for Granger Causality Using Self-explaining Neural Networks.

Ricards Marcinkevics, Julia E. Vogt

Proceedings of the 9th International Conference on Learning Representations, 2021

Exploring Relationships between Cerebral and Peripheral Biosignals with Neural Networks.

Alexander H. Hatteland, Ricards Marcinkevics, Renaud Marquis, Thomas Frick, Ilona Hubbard, Julia E. Vogt, Thomas Brunschwiler, Philippe Ryvlin

Proceedings of the IEEE International Conference on Digital Health, 2021

2020

Interpretability and Explainability: A Machine Learning Zoo Mini-tour.

Ricards Marcinkevics, Julia E. Vogt

CoRR, 2020

Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models.

Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt

Proceedings of the Pattern Recognition - 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28, 2020

2019

Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm.

Ricards Marcinkevics, Steven Kelk, Carlo Galuzzi, Berthold Stegemann

CoRR, 2019