1 code implementation • 12 Feb 2024 • Meng-Chieh Lee, Haiyang Yu, Jian Zhang, Vassilis N. Ioannidis, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos
Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well?
1 code implementation • 20 Apr 2023 • Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang, Da Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos, George Karypis
Different from conventional knowledge distillation, GRAD jointly optimizes a GNN teacher and a graph-free student over the graph's nodes via a shared LM.
1 code implementation • 24 Feb 2023 • Shichang Zhang, Jiani Zhang, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos, Yizhou Sun
However, GNN explanation for link prediction (LP) is lacking in the literature.
no code implementations • 31 Jan 2023 • Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
no code implementations • 9 Jun 2022 • Zhenwei Dai, Vasileios Ioannidis, Soji Adeshina, Zak Jost, Christos Faloutsos, George Karypis
ScatterSample employs a sampling module termed DiverseUncertainty to collect instances with large uncertainty from different regions of the sample space for labeling.
1 code implementation • 10 Dec 2021 • Costas Mavromatis, Prasanna Lakkur Subramanyam, Vassilis N. Ioannidis, Soji Adeshina, Phillip R. Howard, Tetiana Grinberg, Nagib Hakim, George Karypis
The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings.
Ranked #1 on Question Answering on CronQuestions
1 code implementation • 26 Oct 2021 • Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf
For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets.
no code implementations • 12 Oct 2021 • Cole Hawkins, Vassilis N. Ioannidis, Soji Adeshina, George Karypis
Consistency training is a popular method to improve deep learning models in computer vision and natural language processing.
no code implementations • ICLR 2022 • Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf
Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e. g., text-based, categorical, continuous, etc.).
no code implementations • 29 Jul 2021 • Jaime D. Acevedo-Viloria, Luisa Roa, Soji Adeshina, Cesar Charalla Olazo, Andrés Rodríguez-Rey, Jose Alberto Ramos, Alejandro Correa-Bahnsen
Large digital platforms create environments where different types of user interactions are captured, these relationships offer a novel source of information for fraud detection problems.