In this talk, we introduce a sampling-based semi-Lagrangian adaptive rank (SLAR) method, which leverages a cross-approximation strategy - or pseudo-skeleton decomposition - to efficiently represent ...

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent ...

Abstract: Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this ...

Abstract: Diffusion and Poisson flow models have shown impressive performance in a wide range of generative tasks, including low-dose CT (LDCT) image denoising. However, one limitation in general, and ...

Hello, I am trying to reproduce phase retrieval results with Poisson noise. I have tried different rates for the noise but I never got a decent result, while results for gaussian noise were good. are ...

I am trying to implement the model, where I have the aggregated counts on a grid. Some counts are low (or even missing) because they are undersampled. I wanted to implement a distance sampling ...

Sampling is a technique in which samples are drawn at random (without any favor or bias). For this, suitable measures or procedures may be laid down and adopted according to the nature and ...

We demonstrate that three of the most prominent accounts of visual working memory in the psychology and neuroscience literature—the slots+averaging model, the variable precision model, and the ...

1 Department of Palaentology and Museum, University of Zurich, Zurich, Switzerland 2 Centre of Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway The ...