CAREER: Enabling Continual Multi-view Representation Learning: An Adversarial Perspective — NSF Award to University of Massachuset
Representation learning techniques attempt to extract and abstract key information (i.e., the features) from raw data to be used in analyses in a wide range of applications, such as cybersecurity, industry, finance, economics, and scientific discovery. As a critical step in machine learning systems, representation lear
| Award title | CAREER: Enabling Continual Multi-view Representation Learning: An Adversarial Perspective |
|---|---|
| Award ID | 2528483 |
| Awardee | University of Massachusetts Lowell |
| City | LOWELL |
| State | MA |
| Amount obligated | $366,883 |
| Principal investigator | Ming Shao |
| Program | Info Integration & Informatics |
| Start date | 01/15/2025 |
| Abstract | Representation learning techniques attempt to extract and abstract key information (i.e., the features) from raw data to be used in analyses in a wide range of applications, such as cybersecurity, industry, finance, economics, and scientific discovery. As a critical step in machine learning systems, representation learning is meant to be robust in its capacity, regardless of the mutation of raw data due to noises or the variations of raw data caused by capture devices. In the era of big data, re |
| Source | NSF Awards |
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