Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …

We perform a careful, thorough, and large scale empirical study of the correspondence
between wide neural networks and kernel methods. By doing so, we resolve a variety of …

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and
theory that have been accumulated over the years. Recently, this field has been immensely …

In this paper we describe an investigation into the application of deep learning methods for
low-dose and sparse angle computed tomography using small training datasets. To motivate …

Recovering a high-quality image from noisy indirect measurements is an important problem
with many applications. For such inverse problems, supervised deep convolutional neural …

Deep image prior (DIP) and its variants have showed remarkable potential for solving
inverse problems in computer vision, without any extra training data. Practical DIP models …

Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems.
Among them, denoising is known to be particularly challenging for the DIP due to noise …

We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography
(DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high …

In recent years, advancements in machine learning (ML) techniques, in particular, deep
learning (DL) methods have gained a lot of momentum in solving inverse imaging problems …