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Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node.…

Machine Learning · Computer Science 2019-01-10 Kumar Shridhar , Felix Laumann , Marcus Liwicki

The Bayesian uncertainty quantification technique has become well established in turbulence modeling over the past few years. However, it is computationally expensive to construct a globally accurate surrogate model for Bayesian inference…

Data Analysis, Statistics and Probability · Physics 2022-03-16 Fanzhi Zeng , Wei Zhang , Jinping Li , Tianxin Zhang , Chao Yan

Proper regularization is crucial in inverse problems to achieve high-quality reconstruction, even with an ill-conditioned measurement system. This is particularly true for three-dimensional photoacoustic tomography, which is computationally…

Optimization and Control · Mathematics 2024-09-26 Chao Wang , Alexandre H. Thiery

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as…

Computer Vision and Pattern Recognition · Computer Science 2017-05-31 Ryutaro Tanno , Daniel E. Worrall , Aurobrata Ghosh , Enrico Kaden , Stamatios N. Sotiropoulos , Antonio Criminisi , Daniel C. Alexander

Positron emission tomography (PET) is an important functional medical imaging technique often used in the evaluation of certain brain disorders, whose reconstruction problem is ill-posed. The vast majority of reconstruction methods in PET…

Image and Video Processing · Electrical Eng. & Systems 2023-06-09 Tin Vlašić , Tomislav Matulić , Damir Seršić

Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of…

Image and Video Processing · Electrical Eng. & Systems 2021-09-14 Khalid L. Alsamadony , Ertugrul U. Yildirim , Guenther Glatz , Umair bin Waheed , Sherif M. Hanafy

Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying…

Computer Vision and Pattern Recognition · Computer Science 2023-07-14 M. Eren Akbiyik

Inverse problems are prevalent across various disciplines in science and engineering. In the field of computer vision, tasks such as inpainting, deblurring, and super-resolution are commonly formulated as inverse problems. Recently,…

Machine Learning · Computer Science 2025-01-07 Shayan Mohajer Hamidi , En-Hui Yang

Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based…

Computational Physics · Physics 2019-12-09 Cristina White , Daniela Ushizima , Charbel Farhat

Autoregressive (AR) language models rely on causal tokenization, but extending this paradigm to vision remains non-trivial. Current visual tokenizers either flatten 2D patches into non-causal sequences or enforce heuristic orderings that…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Yitong Chen , Zuxuan Wu , Xipeng Qiu , Yu-Gang Jiang

Established image recovery methods in fast ultrasound imaging, e.g. delay-and-sum, trade the image quality for the high frame rate. Cutting-edge inverse scattering methods based on compressed sensing (CS) disrupt this tradeoff via a priori…

Medical Physics · Physics 2019-03-15 Martin F. Schiffner

Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters. Traditional Bayesian calibration involves the emulation of the computer model and an additive model…

Machine Learning · Statistics 2018-10-30 Sébastien Marmin , Maurizio Filippone

In this paper, we study porous media flows in heterogeneous stochastic media. We propose an efficient forward simulation technique that is tailored for variational Bayesian inversion. As a starting point, the proposed forward simulation…

Applications · Statistics 2018-02-12 Keren Yang , Nilabja Guha , Yalchin Efendiev , Bani K. Mallick

Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images.…

Methodology · Statistics 2022-10-06 Meng Li , Li Ma

Generative models excel at synthesizing high-fidelity samples from complex data distributions, but they often violate hard constraints arising from physical laws or task specifications. A common remedy is to project intermediate samples…

Machine Learning · Computer Science 2025-09-30 Jinhao Liang , Yixuan Sun , Anirban Samaddar , Sandeep Madireddy , Ferdinando Fioretto

We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which uses a fixed mean-squared-error criterion, our approach introduces a learned…

Machine Learning · Computer Science 2026-04-14 Shanchuan Lin , Ceyuan Yang , Zhijie Lin , Hao Chen , Haoqi Fan

Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weight-based BNNs often struggle with high computational…

Machine Learning · Statistics 2022-06-13 Trung Trinh , Markus Heinonen , Luigi Acerbi , Samuel Kaski

In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime. As motivation, we review some of the existing approaches and obtain quantitative results after…

Image and Video Processing · Electrical Eng. & Systems 2021-04-20 Daniel Otero Baguer , Johannes Leuschner , Maximilian Schmidt

Bayesian Neural Networks (BayNNs) naturally provide uncertainty in their predictions, making them a suitable choice in safety-critical applications. Additionally, their realization using memristor-based in-memory computing (IMC)…

Machine Learning · Computer Science 2024-01-24 Soyed Tuhin Ahmed , Kamal Danouchi , Guillaume Prenat , Lorena Anghel , Mehdi B. Tahoori

Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved challenging in practice. Addressing the drawbacks of existing…

Machine Learning · Statistics 2023-12-29 Tim G. J. Rudner , Freddie Bickford Smith , Qixuan Feng , Yee Whye Teh , Yarin Gal
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