Search results
Results: 7
Number of items: 7
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Knigge, D. M., Romero, D. W., Gu, A., Gavves, E., Bekkers, E. J., Tomczak, J. M., Hoogendoorn, M., & Sonke, J.-J. (2023). Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN. In The Eleventh International Conference on Learning Representations: ICLR 2023 https://openreview.net/forum?id=ZW5aK4yCRqU -
Romero, D. W., Bruintjes, R.-J., Bekkers, E. J., Tomczak, J. M., Hoogendoorn, M., & van Gemert, J. C. (2022). FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes. In The Tenth International Conference on Learning Representations: ICLR 2022 https://openreview.net/forum?id=3jooF27-0Wy
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Romero, D. W., Kuzina, A., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2022). CKConv: Continuous Kernel Convolution For Sequential Data. In The Tenth International Conference on Learning Representations: ICLR 2022 https://doi.org/10.48550/arXiv.2102.02611
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Dam, T. A., Hoogendoorn, M., Elbers, P. W. G., & Dutch ICU Data Sharing Against COVID-19 Collaborators (2022). Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning. Annals of intensive care, 12, Article 99. https://doi.org/10.1186/s13613-022-01070-0
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Fleuren, L. M., Fornasa, M., Cinà, G., Hoogendoorn, M., Elbers, P. W. G., & Dutch ICU Data Sharing Against COVID-19 Collaborators (2021). Predictors for extubation failure in COVID-19 patients using a machine learning approach. Critical Care, 25, Article 448. https://doi.org/10.1186/s13054-021-03864-3
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Romero, D. W., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2020). Attentive group equivariant convolutional networks. Proceedings of Machine Learning Research, 119, 8188-8199. http://proceedings.mlr.press/v119/romero20a.html
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Romero, D. W., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2020). Wavelet networks: Scale equivariant learning from raw waveforms. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2006.05259
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