Sancar Adali

TL;DR: The state-of-the-art approximategraph matching algorithm "FAQ" of Vogelstein et al. (2015) is modified to make it a fast approximate seeded graph matching algorithm, adapt its applicability to include graphs with differently sized vertex sets, and extend the algorithm so as to provide, for each individual vertex, a nomination list of likely matches.

TL;DR: In this article, a fast approximate seeded graph matching algorithm is proposed to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs given a partial alignment that we are tasked with completing.

TL;DR: In this paper, the authors investigate the manifold matching problem from the perspective of jointly optimizing the fidelity of the embeddings and their commensurability with one another, with a specific statistical inference exploitation task in mind, and demonstrate when and why their joint optimization methodology is superior to either version of separate optimization.

TL;DR: Li et al. as discussed by the authors proposed a two-stream 3D convolutional neural network architecture by introducing the discriminative code layer and the corresponding discrimINative code loss function. And the proposed network processes IR images and the IR-based optical flow field sequences.

TL;DR: A novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm and demonstrates the versatility of the algorithm in matching graphs with various characteristics--weightedness, directedness, loopiness, many-to-one and many- to-many matchings, and soft seedings.