Bastiaan S. Veeling

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 25

Top Topics

Signal Recovery

Compressed Sensing

Information Bottleneck

Top Venues

Publications

Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
Improved motif-scaffolding with SE(3) flow matching. Trans. Mach. Learn. Res. 2024 (2024)
Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
Improved motif-scaffolding with SE(3) flow matching. CoRR (2024)
Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. ICLR (2024)
Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. CoRR (2023)
Marco Federici, Patrick Forré, Ryota Tomioka, Bastiaan S. Veeling
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck. CoRR (2023)
Stojan Trajanovski, Dimitrios Mavroeidis, Christine Leon Swisher, Binyam Gebrekidan Gebre, Bastiaan S. Veeling, Rafael Wiemker, Tobias Klinder, Amir Tahmasebi, Shawn M. Regis, Christoph Wald, Brady J. McKee, Sebastian Flacke, Heber MacMahon, Homer H. Pien
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning. Comput. Medical Imaging Graph. 90 (2021)
Lizeth Gonzalez-Carabarin, Iris A. M. Huijben, Bastiaan S. Veeling, Alexandre Schmid, Ruud J. G. van Sloun
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities. CoRR (2021)
Hans Van Gorp, Iris A. M. Huijben, Bastiaan S. Veeling, Nicola Pezzotti, Ruud J. G. van Sloun
Active Deep Probabilistic Subsampling. ICML (2021)
Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI. ICASSP (2020)
Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton
Hydra: Preserving Ensemble Diversity for Model Distillation. CoRR (2020)
Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI. CoRR (2020)
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. CoRR (2020)
Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun
Deep probabilistic subsampling for task-adaptive compressed sensing. ICLR (2020)
Iris A. M. Huijben, Bastiaan S. Veeling, Kees Janse, Massimo Mischi, Ruud J. G. van Sloun
Learning Sub-Sampling and Signal Recovery With Applications in Ultrasound Imaging. IEEE Trans. Medical Imaging 39 (12) (2020)
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. ICML (2020)
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML (2020)
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
How Good is the Bayes Posterior in Deep Neural Networks Really? CoRR (2020)
Shi Hu, Daniel E. Worrall, Stefan Knegt, Bastiaan S. Veeling, Henkjan J. Huisman, Max Welling
Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. CoRR (2019)
Iris A. M. Huijben, Bastiaan S. Veeling, Kees Janse, Massimo Mischi, Ruud J. G. van Sloun
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging. CoRR (2019)
Sindy Löwe, Peter O'Connor, Bastiaan S. Veeling
Putting An End to End-to-End: Gradient-Isolated Learning of Representations. NeurIPS (2019)
Sindy Löwe, Peter O'Connor, Bastiaan S. Veeling
Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning. CoRR (2019)
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling
Rotation Equivariant CNNs for Digital Pathology. CoRR (2018)
Jasper Linmans, Jim Winkens, Bastiaan S. Veeling, Taco S. Cohen, Max Welling
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks. CoRR (2018)
Bastiaan S. Veeling, Rianne van den Berg, Max Welling
Predictive Uncertainty through Quantization. CoRR (2018)
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling
Rotation Equivariant CNNs for Digital Pathology. MICCAI (2) (2018)