Search Results for author:
Found 31 papers, 16 papers with code
Online learning in motion modeling for intra-interventional image sequences
Image monitoring and guidance during medical examinations can aid both diagnosis and treatment.
Taming Diffusion Models for Image Restoration: A Review
Diffusion models have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences.
Conditional sampling within generative diffusion models
Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those found in image processing and language models.
Learning incomplete factorization preconditioners for GMRES
Incomplete LU factorizations of sparse matrices are widely used as preconditioners in Krylov subspace methods to speed up solving linear systems.
Conditioning diffusion models by explicit forward-backward bridging
Given an unconditional diffusion model $\pi(x, y)$, using it to perform conditional simulation $\pi(x \mid y)$ is still largely an open question and is typically achieved by learning conditional drifts to the denoising SDE after the fact.
Efficient Radiation Treatment Planning based on Voxel Importance
We propose an approach to reduce the large optimization problem by only using a representative subset of informative voxels.
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models
Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets.
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model
Reducing a graph while preserving its overall structure is an important problem with many applications.
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
We show that such an SDE has a solution that we can use to calculate the log probability of the policy, yielding an entropy regularizer that improves the exploration of offline datasets.
Variational Elliptical Processes
We present elliptical processes, a family of non-parametric probabilistic models that subsume Gaussian processes and Student's t processes.
On Feynman--Kac training of partial Bayesian neural networks
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 networks.
Controlling Vision-Language Models for Multi-Task Image Restoration
In this paper, we present a degradation-aware vision-language model (DA-CLIP) to better transfer pretrained vision-language models to low-level vision tasks as a multi-task framework for image restoration.
Ranked #2 on
Single Image Deraining
on Rain100H
ICML 2023 Topological Deep Learning Challenge : Design and Results
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning.
Personalized Privacy Amplification via Importance Sampling
We first consider the general case where an arbitrary personalized differentially private mechanism is subsampled with an arbitrary importance sampling distribution and show that the resulting mechanism also satisfies personalized differential privacy.
Risk-sensitive Actor-free Policy via Convex Optimization
Traditional reinforcement learning methods optimize agents without considering safety, potentially resulting in unintended consequences.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
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 improved by preconditioning.
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
This work aims to improve the applicability of diffusion models in realistic image restoration.
Archetypal Analysis++: Rethinking the Initialization Strategy
Archetypal analysis is a matrix factorization method with convexity constraints.
Image Restoration with Mean-Reverting Stochastic Differential Equations
This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration.
Ranked #3 on
Single Image Deraining
on Rain100H
A Tutorial on Parametric Variational Inference
Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model.
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
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 accessible.
NUQ: A Noise Metric for Diffusion MRI via Uncertainty Discrepancy Quantification
However, it is hard to estimate the impact of noise on downstream tasks based only on such qualitative assessments.
Graph-based Neural Acceleration for Nonnegative Matrix Factorization
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 in certain fields, e. g., sparse linear algebra, but has not yet been exploited to design graph neural networks for matrix computations.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Unsupervised dynamic modeling of medical image transformation
For this, we use a conditional variational auto-encoder (CVAE) to nonlinearly map the higher-dimensional image to a lower-dimensional space, wherein we model the dynamics with a linear Gaussian state-space model (LG-SSM).
Decentralized Differentially Private Segmentation with PATE
For this reason, we turn our attention to Private Aggregation of Teacher Ensembles (PATE), where all local models can be trained independently without inter-institutional communication.
Registration by tracking for sequential 2D MRI
Together with a sparse-to-dense interpolation scheme we can then estimate of the displacement field.
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes
We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process.
A unified representation network for segmentation with missing modalities
The second is the unified representation network: a network architecture that maps a variable number of input modalities into a unified representation that can be used for downstream tasks such as segmentation.
Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space.
