79 citations · 331 across the 13 of their papers we have counts for
13 papers
Improved motif-scaffolding with SE(3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu +9
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models…
Fast protein backbone generation with SE(3) flow matching
Jason Yim, Andrew Campbell, Andrew Y. K. Foong +8
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-mat…
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
Marco Federici, Patrick Forré, Ryota Tomioka +1
Markov processes are widely used mathematical models for describing dynamic systems in various fields. However, accurately simulating large-scale systems at long time scales is com…
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris +2
Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniqu…
Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
Iris A. M. Huijben, Bastiaan S. Veeling, Ruud J. G. van Sloun
Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality. Consequently, the design of optimal sa…
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling +7
Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods…
How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling +7
During the past five years the Bayesian deep learning community has developed increasingly accurate and efficient approximate inference procedures that allow for Bayesian inference…
Hydra: Preserving Ensemble Diversity for Model Distillation
Linh Tran, Bastiaan S. Veeling, Kevin Roth +7
Ensembles of models have been empirically shown to improve predictive performance and to yield robust measures of uncertainty. However, they are expensive in computation and memory…
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
Iris A. M. Huijben, Bastiaan S. Veeling, Kees Janse +2
Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) co…
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
Sindy Löwe, Peter O'Connor, Bastiaan S. Veeling
We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order…
Predictive Uncertainty through Quantization
Bastiaan S. Veeling, Rianne van den Berg, Max Welling
High-risk domains require reliable confidence estimates from predictive models. Deep latent variable models provide these, but suffer from the rigid variational distributions used…
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
Jasper Linmans, Jim Winkens, Bastiaan S. Veeling +2
We propose a semantic segmentation model that exploits rotation and reflection symmetries. We demonstrate significant gains in sample efficiency due to increased weight sharing, as…
Rotation Equivariant CNNs for Digital Pathology
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens +2
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing rec…