We introduce primal and dual stochastic gradient oracle methods for decentralized convex
optimization problems. Both for primal and dual oracles, the proposed methods are optimal …

Interacting many-body physical systems ranging from neural networks in the brain to folding
proteins to self-modifying electrical circuits can learn to perform diverse tasks. This learning …

Reconstructing under-sampled k-space measurements in Compressed Sensing MRI (CS-
MRI) is classically solved by minimizing a regularized least-squares cost function. In the …

Procedural modeling allows for an automatic generation of large amounts of similar assets,
but there is limited control over the generated output. We address this problem by …

Abstract Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-
sampled MR images, offering the potential to reduce scan times. Classical techniques …

Evidence-based decision-making entails collecting (costly) observations about an
underlying phenomenon of interest, and subsequently committing to an (informed) decision …

We propose using Lévy α-stable distributions to construct priors for Bayesian inverse
problems. The construction is based on Markov fields with stable-distributed increments …

Inverse problems can be challenging and interesting to study in the context of metallurgical
processes. This work aims to carry out a method for inverse modeling for simultaneous …

The problem of reconstruction of a timewise dependent coefficient and free boundary at
once in a nonlocal diffusion equation under Stefan and heat Flux as nonlocal …

Recent works in deep learning have shown that integrating differentiable physics simulators
into the training process can greatly improve the quality of results. Although this combination …