In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Molecular dynamics (MD) simulations employing classical force fields constitute the
cornerstone of contemporary atomistic modeling in chemistry, biology, and materials …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

We present an optimized implementation of the recently proposed symmetric gradient
domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce …

We propose a scalable Gromov-Wasserstein learning (S-GWL) method and establish a
novel and theoretically-supported paradigm for large-scale graph analysis. The proposed …

Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix,
which can be generally formulated as Lawler's quadratic assignment problem (QAP). This …

The success of kernel methods has initiated the design of novel positive semidefinite
functions, in particular for structured data. A leading design paradigm for this is the …

We present a multi-robot system for GPS-denied search and rescue under the forest canopy.
Forests are particularly challenging environments for collaborative exploration and mapping …