Kohn–Sham Density Functional Theory (KSDFT) is the most widely used electronic structure
method in chemistry, physics, and materials science, with thousands of calculations cited …

To overcome the limitations associated with purely physics-based and data-driven modeling
methods, hybrid, physics-based data-driven models have been developed, with improved …

Global modeling-based image restoration frameworks have become popular. However, they
often require a high memory footprint and do not consider task-specific degradation. Our …

We describe a simple method for unsupervised domain adaptation, whereby the
discrepancy between the source and target distributions is reduced by swapping the low …

Pan-sharpening aims to generate high-resolution multi-spectral (MS) images by fusing PAN
images and low-resolution MS images. Despite its great advances, most existing pan …

There is an increasing need to bring machine learning to a wide diversity of hardware
devices. Current frameworks rely on vendor-specific operator libraries and optimize for a …

We introduce a learning-based framework to optimize tensor programs for deep learning
workloads. Efficient implementations of tensor operators, such as matrix multiplication and …

Deep learning models with convolutional and recurrent networks are now ubiquitous and
analyze massive amounts of audio, image, video, text and graph data, with applications in …

The spectral analysis of signals is currently either dominated by the speed–accuracy trade-
off or ignores a signal's often non-stationary character. Here we introduce an open-source …

High-performance tensor programs are crucial to guarantee efficient execution of deep
neural networks. However, obtaining performant tensor programs for different operators on …