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pro vyhledávání: '"CACERES, RAJMONDA S."'
Autor:
Shafi, Zohair, Miller, Benjamin A., Chatterjee, Ayan, Eliassi-Rad, Tina, Caceres, Rajmonda S.
Recent advances in machine learning (ML) have shown promise in aiding and accelerating classical combinatorial optimization algorithms. ML-based speed ups that aim to learn in an end to end manner (i.e., directly output the solution) tend to trade of
Externí odkaz:
http://arxiv.org/abs/2310.07980
Machine learning (ML) approaches are increasingly being used to accelerate combinatorial optimization (CO) problems. We investigate the Set Cover Problem (SCP) and propose Graph-SCP, a graph neural network method that augments existing optimization s
Externí odkaz:
http://arxiv.org/abs/2310.07979
The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years. We posit that the traditional system analysis perspective is needed when designing and implementing ML algorithms and systems.
Externí odkaz:
http://arxiv.org/abs/2204.08836
In this paper, we present a novel approach based on the random walk process for finding meaningful representations of a graph model. Our approach leverages the transient behavior of many short random walks with novel initialization mechanisms to gene
Externí odkaz:
http://arxiv.org/abs/1704.05516
Autor:
Fish, Benjamin, Caceres, Rajmonda S.
For any stream of time-stamped edges that form a dynamic network, an important choice is the aggregation granularity that an analyst uses to bin the data. Picking such a windowing of the data is often done by hand, or left up to the technology that i
Externí odkaz:
http://arxiv.org/abs/1702.07752
Word embedding models offer continuous vector representations that can capture rich contextual semantics based on their word co-occurrence patterns. While these word vectors can provide very effective features used in many NLP tasks such as clusterin
Externí odkaz:
http://arxiv.org/abs/1702.07680
Autor:
Caceres, Rajmonda S., Weiner, Leah, Schmidt, Matthew C., Miller, Benjamin A., Campbell, William M.
Graphs are powerful abstractions for capturing complex relationships in diverse application settings. An active area of research focuses on theoretical models that define the generative mechanism of a graph. Yet given the complexity and inherent nois
Externí odkaz:
http://arxiv.org/abs/1609.04859
Publikováno v:
Phys. Rev. X 7, 031056 (2017)
Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into wi
Externí odkaz:
http://arxiv.org/abs/1609.04376
Autor:
Fish, Benjamin, Caceres, Rajmonda S.
Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversampling can affect the quality of many impor
Externí odkaz:
http://arxiv.org/abs/1504.06667
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