Self-supervised Probabilistic Graph Structure Learning for Task-agnostic Latent Representa — NSF Award to University of Texas at A
Graphs provide simple and yet powerful mathematical structures to describe pairwise connections among different parties while providing a natural way to develop a deep understanding for real-world environments. There are many situations, however, where graph connections are not readily apparent or are completely hidden
| Award title | Self-supervised Probabilistic Graph Structure Learning for Task-agnostic Latent Representa |
|---|---|
| Award ID | 2407692 |
| Awardee | University of Texas at Arlington |
| City | ARLINGTON |
| State | TX |
| Amount obligated | $229,461 |
| Principal investigator | Li Wang |
| Program | OFFICE OF MULTIDISCIPLINARY AC, APPLIED MATHEMATICS |
| Start date | 09/01/2024 |
| Abstract | Graphs provide simple and yet powerful mathematical structures to describe pairwise connections among different parties while providing a natural way to develop a deep understanding for real-world environments. There are many situations, however, where graph connections are not readily apparent or are completely hidden. For example, hidden within mountainous microarray data from breast cancer are tree-structure graphs that can delineate breast cancer progressions from one stage to another and th |
| Source | NSF Awards |
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