Zobrazeno 1 - 10
of 16 204
pro vyhledávání: '"Lee, Soo In"'
Autor:
Kim, Sunwoo, Lee, Soo Yong, Bu, Fanchen, Kang, Shinhwan, Kim, Kyungho, Yoo, Jaemin, Shin, Kijung
Graph autoencoders (Graph-AEs) learn representations of given graphs by aiming to accurately reconstruct them. A notable application of Graph-AEs is graph-level anomaly detection (GLAD), whose objective is to identify graphs with anomalous topologica
Externí odkaz:
http://arxiv.org/abs/2410.20366
Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to visualize brain activation regions by detecting hemodynamic responses associated with increased metabolic demand. While alternative MRI methods ha
Externí odkaz:
http://arxiv.org/abs/2409.07806
Combinatorial optimization (CO) is naturally discrete, making machine learning based on differentiable optimization inapplicable. Karalias & Loukas (2020) adapted the probabilistic method to incorporate CO into differentiable optimization. Their work
Externí odkaz:
http://arxiv.org/abs/2405.08424
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are
Externí odkaz:
http://arxiv.org/abs/2404.01039
Hypergraphs are marked by complex topology, expressing higher-order interactions among multiple nodes with hyperedges, and better capturing the topology is essential for effective representation learning. Recent advances in generative self-supervised
Externí odkaz:
http://arxiv.org/abs/2404.00638
Autor:
Lee, Soo Teck, Zhang, Ruibin
We develop an algebraic approach to the branching of representations of the general linear Lie superalgebra $\mathfrak{gl}_{p|q}({\mathbb C})$, by constructing certain super commutative algebras whose structure encodes the branching rules. Using this
Externí odkaz:
http://arxiv.org/abs/2403.11393
How would randomly shuffling feature vectors among nodes from the same class affect graph neural networks (GNNs)? The feature shuffle, intuitively, perturbs the dependence between graph topology and features (A-X dependence) for GNNs to learn from. S
Externí odkaz:
http://arxiv.org/abs/2402.04621
Graph neural networks (GNNs) learn the representation of graph-structured data, and their expressiveness can be further enhanced by inferring node relations for propagation. Attention-based GNNs infer neighbor importance to manipulate the weight of i
Externí odkaz:
http://arxiv.org/abs/2306.02376
The Targeted Free Energy Perturbation (TFEP) method aims to overcome the time-consuming and computer-intensive stratification process of standard methods for estimating the free energy difference between two states. To achieve this, TFEP uses a mappi
Externí odkaz:
http://arxiv.org/abs/2302.11855
Publikováno v:
Advanced Science. 10/16/2024, Vol. 11 Issue 38, p1-13. 13p.
