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pro vyhledávání: '"Mai, Tiep"'
Autor:
Lam, Hoang Thanh, Thiebaut, Johann-Michael, Sinn, Mathieu, Chen, Bei, Mai, Tiep, Alkan, Oznur
Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from raw data. In this paper, we in
Externí odkaz:
http://arxiv.org/abs/1706.00327
Entity disambiguation, or mapping a phrase to its canonical representation in a knowledge base, is a fundamental step in many natural language processing applications. Existing techniques based on global ranking models fail to capture the individual
Externí odkaz:
http://arxiv.org/abs/1604.05875
Autor:
Mai, Tiep, Wilson, Simon
A method for sequential inference of the fixed parameters of a dynamic latent Gaussian models is proposed and evaluated that is based on the iterated Laplace approximation. The method provides a useful trade-off between computational performance and
Externí odkaz:
http://arxiv.org/abs/1509.07900
Autor:
Mai, Tiep, Wilson, Simon
In this paper, several modifications are introduced to the functional approximation method iterLap to reduce the approximation error, including stopping rule adjustment, proposal of new residual function, starting point selection for numerical optimi
Externí odkaz:
http://arxiv.org/abs/1509.06492
Understanding user behavior is essential to personalize and enrich a user's online experience. While there are significant benefits to be accrued from the pursuit of personalized services based on a fine-grained behavioral analysis, care must be take
Externí odkaz:
http://arxiv.org/abs/1504.01781
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