5 accepted papers
Reasoning problems such as Sudoku and ARC-AGI remain challenging for neural networks. Recurrent Reasoning Models (RRMs), including Hierarchical Reasoning Models (HRM) and Tiny Recursive Models (TRM), offer a compact alternative to large language models, but currently handle symbol symmetries only im…
State-of-the-art image reconstruction often relies on complex, abundantly parameterized deep architectures. We propose an alternative: a data-driven reconstruction method inspired by the classic Tikhonov regularization. Our approach iteratively refines intermediate reconstructions by solving a seque…
"In this paper, we propose a novel method for color propagation that is used to recolor gray-scale videos (e.g. historic movies). Our energy-based model combines deep learning with a variational formulation. At its core, the method optimizes over a set of plausible color proposals that are extracted…
Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term. In this paper, we propose a novel learnable general-purpose regularizer exploiting recent architectural design patterns from deep learning. We cast the le…
In this work, we propose a learning-based variational network (VN) approach for reconstruction of low-dose 3D computed tomography data. We focus on two methods to decrease the radiation dose: (1) x-ray tube current reduction, which reduces the signal-to-noise ratio, and (2) x-ray beam interruption,…