DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science
In this paper, the history, present status, and future of density-functional theory (DFT) is
informally reviewed and discussed by 70 workers in the field, including molecular scientists …
informally reviewed and discussed by 70 workers in the field, including molecular scientists …
Toward a theoretical foundation of policy optimization for learning control policies
Gradient-based methods have been widely used for system design and optimization in
diverse application domains. Recently, there has been a renewed interest in studying …
diverse application domains. Recently, there has been a renewed interest in studying …
Score-based diffusion models for accelerated MRI
Score-based diffusion models provide a powerful way to model images using the gradient of
the data distribution. Leveraging the learned score function as a prior, here we introduce a …
the data distribution. Leveraging the learned score function as a prior, here we introduce a …
Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction
Diffusion models have recently attained significant interest within the community owing to
their strong performance as generative models. Furthermore, its application to inverse …
their strong performance as generative models. Furthermore, its application to inverse …
Hopfield networks is all you need
We introduce a modern Hopfield network with continuous states and a corresponding
update rule. The new Hopfield network can store exponentially (with the dimension of the …
update rule. The new Hopfield network can store exponentially (with the dimension of the …
Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications
Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving
computational imaging problems through the integration of physical models and learned …
computational imaging problems through the integration of physical models and learned …
Computational optimal transport: With applications to data science
Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
A modern introduction to online learning
F Orabona - arXiv preprint arXiv:1912.13213, 2019 - arxiv.org
In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …
of Online Convex Optimization. Here, online learning refers to the framework of regret …
OSQP: An operator splitting solver for quadratic programs
We present a general-purpose solver for convex quadratic programs based on the
alternating direction method of multipliers, employing a novel operator splitting technique …
alternating direction method of multipliers, employing a novel operator splitting technique …
Elastic graph neural networks
While many existing graph neural networks (GNNs) have been proven to perform $\ell_2 $-
based graph smoothing that enforces smoothness globally, in this work we aim to further …
based graph smoothing that enforces smoothness globally, in this work we aim to further …