Daniel Lokshtanov’s work explores the limits of what computers can solve, paving the way for advances in artificial intelligence and computational efficiency.
UC Santa Barbara computer scientist Daniel Lokshtanov is advancing fundamental understanding of computational efficiency through groundbreaking research on quasi-polynomial time algorithms, supported ...
Improved Modeling and Generalization Capabilities of Graph Neural Networks With Legendre Polynomials
Abstract: LegendreNet is a novel graph neural network (GNNs) model that addresses stability issues present in traditional GNN models such as ChebNet, while also more effectively capturing higher-order ...
Abstract: Label distribution learning (LDL), leveraging the label significance (LS), is more appropriate for solving label ambiguity problems than multilabel learning (MLL). However, directly ...
At the University of Nevada, Reno, we believe in the power of purpose and the strength of the Pack. Whether you're engineering clean energy, studying literature’s role in social change, or designing ...
Michael Boyle is an experienced financial professional with more than 10 years working with financial planning, derivatives, equities, fixed income, project management, and analytics. David is ...
1 Exact string matching (Z-algorithm, Knuth-Morris-Pratt, Boyer-Moore, Rabin-Karp) Introduction The Z Algorithm Knuth-Morris-Pratt and Boyer-Moore Seminumerical matching: Rabin-Karp & Shift-And 2 ...
This work describes a highly complex automated algorithm for analyzing vascular imaging data from two-photon microscopy. This tool has the potential to be extremely valuable to the field and to fill ...
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