Reducing a graph while preserving its overall structure is an important problem with many
applications. Typically, reduction approaches either remove edges (sparsification) or merge …

Deep Neural Networks (DNNs) have achieved tremendous success in the past few years.
However, their training and inference demand exceptional computational and memory …

Analog dynamical accelerators (DXs) are a growing sub-field in computer architecture
research, offering order-of-magnitude gains in power efficiency and latency over traditional …

Abstract Machine learning has evolved significantly recently and has penetrated every
aspect of science, technology, and daily life. As application prediction demands higher …

Solving equations is fundamental to human understanding of the world. While modern
machine learning methods are powerful equation solvers, their escalating complexity and …