
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | September 7, 2019 |
Latest Amendment Date: | September 7, 2019 |
Award Number: | 1910983 |
Award Instrument: | Standard Grant |
Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2019 |
End Date: | September 30, 2023 (Estimated) |
Total Intended Award Amount: | $298,370.00 |
Total Awarded Amount to Date: | $298,370.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
201 PRESIDENTS CIR SALT LAKE CITY UT US 84112-9049 (801)581-6903 |
Sponsor Congressional District: |
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Primary Place of Performance: |
50 S Central Campus Dr. Rm 3190 Salt Lake City UT US 84112-9205 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Info Integration & Informatics |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Many applications in the real world, such as online
shopping, recommendation, social media and information
security, involve interactions among different
entities. For example, online shopping behaviors can
be simply described by the interactions between
customers, commodities and shopping web sites. These
interactions are naturally represented by tensors,
which are arrays of multiple dimensions. Each
dimension represents a type of entities (e.g.,
customers or commodities), and each element describes
a particular interaction (e.g, purchased/not
purchased). The project aims to develop flexible and
efficient tensor decomposition approaches that can
discover a variety of complicated relationships
between the entities in tensors, handle a tremendous
amount of data from practical applications, and adapt
to rapid data growth. The developed approaches can be
used to promote many important prediction and
knowledge discovery tasks, such as improving the
recommendation accuracy, predicting advertisement
click rates, understanding how misinformation propagation
through social media, and detecting malicious cell-
phone apps.
Despite the success of the existing tensor
decomposition approaches, they use multilinear
decomposition forms or shallow kernels, and are
incapable of capturing highly complicated
relationships in data. However, complex and nonlinear
relationships, effects and patterns are ubiquitous,
due to the diversity and complexity of the practical
applications. Furthermore, there is a lack of
efficient, scalable nonlinear decomposition algorithms
to handle static tensors nowadays at unprecedented
scales, and dynamic tensors that grow fast and
continuously. The project aims to develop scalable
deep Bayesian tensor decomposition approaches that
maximize the flexibility to capture all kinds of
complex relationships, efficiently process static data
at unprecedented scales and rapid data streams, and
provide uncertainty quantification for both embedding
estimations and predictions. The research will be
accomplished through: (1) the design of new Bayesian
tensor decomposition models that incorporate deep
architectures to improve the capability of estimating
intricate functions, (2) the development of
decentralized, asynchronous learning algorithms to
process extremely large-scale static tensors, (3) the
development of online incremental learning algorithms
to handle rapid data streams and to produce responsive
updates upon receiving new data, without retraining
from scratch, and (4) comprehensive evaluations on
both synthetic and real-world big data. The proposed
research will contribute a markedly improved tensor
decomposition toolset that are powerful to estimate
arbitrarily complex relationships, scalable to static
tensors at unprecedented scales (e.g., billions of
nodes and trillions of entries) and to fast data
streams with efficient incremental updates. Moreover,
as Bayesian approaches, the toolset are resilient to
noise, provide posterior distributions for
uncertainty quantification, and integrate all possible
outcomes into robust predictions. Once the toolsets are
available, the understanding of the high-order
relationships in tensors, and the mining of associated
patterns, such as communities and anomalies, will be
enormously enhanced; the predictive performance for
the quantify of interests, such as social links,
click-through-rates, and recommendation, will be
dramatically promoted.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
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This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Last Modified: 01/15/2024
Modified by: Shandian Zhe
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