Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Fertig, Emily"'
Autor:
Song, Xingyou, Zhang, Qiuyi, Lee, Chansoo, Fertig, Emily, Huang, Tzu-Kuo, Belenki, Lior, Kochanski, Greg, Ariafar, Setareh, Vasudevan, Srinivas, Perel, Sagi, Golovin, Daniel
Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a large-scale service. Over multiple years, its algorithm has been improve
Externí odkaz:
http://arxiv.org/abs/2408.11527
Normalizing flows have shown great success as general-purpose density estimators. However, many real world applications require the use of domain-specific knowledge, which normalizing flows cannot readily incorporate. We propose embedded-model flows
Externí odkaz:
http://arxiv.org/abs/2110.06021
Autor:
Ambrogioni, Luca, Lin, Kate, Fertig, Emily, Vikram, Sharad, Hinne, Max, Moore, Dave, van Gerven, Marcel
Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach depends on the choice of an appropriate variational family. Here, we i
Externí odkaz:
http://arxiv.org/abs/2002.00643
Autor:
Ren, Jie, Liu, Peter J., Fertig, Emily, Snoek, Jasper, Poplin, Ryan, DePristo, Mark A., Dillon, Joshua V., Lakshminarayanan, Balaji
Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but con
Externí odkaz:
http://arxiv.org/abs/1906.02845
Autor:
Ovadia, Yaniv, Fertig, Emily, Ren, Jie, Nado, Zachary, Sculley, D, Nowozin, Sebastian, Dillon, Joshua V., Lakshminarayanan, Balaji, Snoek, Jasper
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}. Quantifying uncertain
Externí odkaz:
http://arxiv.org/abs/1906.02530
Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information. For example, variational autoencoders with autoregressive decoders often collapse
Externí odkaz:
http://arxiv.org/abs/1905.07478
In this paper, we investigate the degree to which the encoding of a $\beta$-VAE captures label information across multiple architectures on Binary Static MNIST and Omniglot. Even though they are trained in a completely unsupervised manner, we demonst
Externí odkaz:
http://arxiv.org/abs/1812.02682
Autor:
Fertig, Emily
Publikováno v:
Dissertations.
Climate change mitigation will require extensive decarbonization of the electricity sector. This thesis addresses both large-scale wind integration (Papers 1-3) and development of new energy technologies (Paper 4) in service of this goal. Compressed
Externí odkaz:
http://repository.cmu.edu/dissertations/212
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1212&context=dissertations
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1212&context=dissertations
