112 citations · 298 across the 25 of their papers we have counts for
26 papers
Interacting particle guidance for sampling reward-tilted generative priors
Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund +1
Inference-time steering adapts pretrained diffusion and flow-based models to new tasks, e.g., to generate samples from a conditional distribution or samples with desired properties…
ESS-Flow: Training-free guidance of flow-based models as inference in source space
Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund +1
Guiding pretrained flow-based generative models for conditional generation or to produce samples with desired target properties enables solving diverse tasks without retraining on…
Online learning in motion modeling for intra-interventional image sequences
Niklas Gunnarsson, Jens Sjölund, Peter Kimstrand +1
Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate t…
Taming Diffusion Models for Image Restoration: A Review
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
Diffusion models have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences. Recently, these models have als…
Conditional sampling within generative diffusion models
Zheng Zhao, Ziwei Luo, Jens Sjölund +1
Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those fou…
Learning incomplete factorization preconditioners for GMRES
Paul Häusner, Aleix Nieto Juscafresa, Jens Sjölund
Incomplete LU factorizations of sparse matrices are widely used as preconditioners in Krylov subspace methods to speed up solving linear systems. Unfortunately, computing the preco…
Conditioning diffusion models by explicit forward-backward bridging
Adrien Corenflos, Zheng Zhao, Simo Särkkä +2
Given an unconditional diffusion model targeting a joint model , using it to perform conditional simulation is still largely an open question and is typicall…
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets. Typically, diffus…
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
Ruoqi Zhang, Ziwei Luo, Jens Sjölund +2
This paper presents advanced techniques of training diffusion policies for offline reinforcement learning (RL). At the core is a mean-reverting stochastic differential equation (SD…
Variational Elliptical Processes
Maria Bånkestad, Jens Sjölund, Jalil Taghia +1
We present elliptical processes, a family of non-parametric probabilistic models that subsume Gaussian processes and Student's t processes. This generalization includes a range of…
On Feynman--Kac training of partial Bayesian neural networks
Zheng Zhao, Sebastian Mair, Thomas B. Schön +1
Recently, partial Bayesian neural networks (pBNNs), which only consider a subset of the parameters to be stochastic, were shown to perform competitively with full Bayesian neural n…
Risk-sensitive Actor-free Policy via Convex Optimization
Ruoqi Zhang, Jens Sjölund
Traditional reinforcement learning methods optimize agents without considering safety, potentially resulting in unintended consequences. In this paper, we propose an optimal actor-…
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
Paul Häusner, Ozan Öktem, Jens Sjölund
The convergence of the conjugate gradient method for solving large-scale and sparse linear equation systems depends on the spectral properties of the system matrix, which can be im…
A Tutorial on Parametric Variational Inference
Jens Sjölund
Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in comp…
Archetypal Analysis++: Rethinking the Initialization Strategy
Sebastian Mair, Jens Sjölund
Archetypal analysis is a matrix factorization method with convexity constraints. Due to local minima, a good initialization is essential, but frequently used initialization methods…
Image Restoration with Mean-Reverting Stochastic Differential Equations
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a…
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
Zheng Zhao, Simo Särkkä, Jens Sjölund +1
We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessib…
Graph-based Neural Acceleration for Nonnegative Matrix Factorization
Jens Sjölund, Maria Bånkestad
We describe a graph-based neural acceleration technique for nonnegative matrix factorization that builds upon a connection between matrices and bipartite graphs that is well-known…
Unsupervised dynamic modeling of medical image transformation
Niklas Gunnarsson, Peter Kimstrand, Jens Sjölund +1
Spatiotemporal imaging has applications in e.g. cardiac diagnostics, surgical guidance, and radiotherapy monitoring, In this paper, we explain the temporal motion by identifying th…
Registration by tracking for sequential 2D MRI
Niklas Gunnarsson, Jens Sjölund, Thomas B. Schön
Our anatomy is in constant motion. With modern MR imaging it is possible to record this motion in real-time during an ongoing radiation therapy session. In this paper we present an…
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes
Maria Bånkestad, Jens Sjölund, Jalil Taghia +1
We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process. This generalization includes a…
A unified representation network for segmentation with missing modalities
Kenneth Lau, Jonas Adler, Jens Sjölund
Over the last few years machine learning has demonstrated groundbreaking results in many areas of medical image analysis, including segmentation. A key assumption, however, is that…
A linear programming approach to inverse planning in Gamma Knife radiosurgery
Jens Sjölund, Stella Riad, Marcus Hennix +1
Leksell Gamma Knife is a stereotactic radiosurgery system that allows fine-grained control of the delivered dose distribution. We describe a new inverse planning approach that both…
Bayesian uncertainty quantification in linear models for diffusion MRI
Jens Sjölund, Anders Eklund, Evren Özarslan +3
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Se…