Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Manavoglu, Eren"'
Autor:
Zuo, Simiao, Liu, Xiaodong, Jiao, Jian, Charles, Denis, Manavoglu, Eren, Zhao, Tuo, Gao, Jianfeng
Transformer models have achieved superior performance in various natural language processing tasks. However, the quadratic computational cost of the attention mechanism limits its practicality for long sequences. There are existing attention variants
Externí odkaz:
http://arxiv.org/abs/2212.08136
In this paper, we focus on unsupervised representation learning for clustering of images. Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data
Externí odkaz:
http://arxiv.org/abs/2105.01289
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a sim
Externí odkaz:
http://arxiv.org/abs/2010.01245
Autor:
Deshmukh, Aniket Anand, Kumar, Abhimanu, Boyles, Levi, Charles, Denis, Manavoglu, Eren, Dogan, Urun
Contextual bandits are a common problem faced by machine learning practitioners in domains as diverse as hypothesis testing to product recommendations. There have been a lot of approaches in exploiting rich data representations for contextual bandit
Externí odkaz:
http://arxiv.org/abs/2003.08485
Self-supervision is key to extending use of deep learning for label scarce domains. For most of self-supervised approaches data transformations play an important role. However, up until now the impact of transformations have not been studied. Further
Externí odkaz:
http://arxiv.org/abs/2002.07384
We present a unified framework for Batch Online Learning (OL) for Click Prediction in Search Advertisement. Machine Learning models once deployed, show non-trivial accuracy and calibration degradation over time due to model staleness. It is therefore
Externí odkaz:
http://arxiv.org/abs/1809.04673
Autor:
Moore, John, Pfeiffer, Joel, Wei, Kai, Iyer, Rishabh, Charles, Denis, Gilad-Bachrach, Ran, Boyles, Levi, Manavoglu, Eren
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models. This introduces a bias in the training data that needs to be addressed. Existing solutions to this problem attemp
Externí odkaz:
http://arxiv.org/abs/1804.06909
Akademický článek
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Autor:
Hillard, Dustin1 dhillard@yahoo-inc.com, Manavoglu, Eren1 erenm@yahoo-inc.com, Raghavan, Hema1 raghavan@yahoo-inc.com, Leggetter, Chris1 cjl@yahoo-inc.com, Cantú-Paz, Erick1 erick@yahoo-inc.com, Iyer, Rukmini1 riyer@yahoo-inc.com
Publikováno v:
Information Retrieval Journal. Jun2011, Vol. 14 Issue 3, p315-336. 22p.
Akademický článek
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