Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Mariet, Zelda"'
Autor:
Tsuruta, Hirofumi, Yamazaki, Hiroyuki, Maeda, Ryota, Tamura, Ryotaro, Wei, Jennifer N., Mariet, Zelda, Phloyphisut, Poomarin, Shimokawa, Hidetoshi, Ledsam, Joseph R., Colwell, Lucy, Imura, Akihiro
Antibodies have become an important class of therapeutic agents to treat human diseases. To accelerate therapeutic antibody discovery, computational methods, especially machine learning, have attracted considerable interest for predicting specific in
Externí odkaz:
http://arxiv.org/abs/2306.03329
Autor:
Tran, Dustin, Liu, Jeremiah, Dusenberry, Michael W., Phan, Du, Collier, Mark, Ren, Jie, Han, Kehang, Wang, Zi, Mariet, Zelda, Hu, Huiyi, Band, Neil, Rudner, Tim G. J., Singhal, Karan, Nado, Zachary, van Amersfoort, Joost, Kirsch, Andreas, Jenatton, Rodolphe, Thain, Nithum, Yuan, Honglin, Buchanan, Kelly, Murphy, Kevin, Sculley, D., Gal, Yarin, Ghahramani, Zoubin, Snoek, Jasper, Lakshminarayanan, Balaji
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical t
Externí odkaz:
http://arxiv.org/abs/2207.07411
Autor:
Wang, Zi, Dahl, George E., Swersky, Kevin, Lee, Chansoo, Mariet, Zelda, Nado, Zachary, Gilmer, Justin, Snoek, Jasper, Ghahramani, Zoubin
Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive real-world functions. Contrary to a common belief that BO is suited to optimizing black-box functions, it actually requires domain knowledge on charact
Externí odkaz:
http://arxiv.org/abs/2207.03084
Ensembles are a straightforward, remarkably effective method for improving the accuracy,calibration, and robustness of models on classification tasks; yet, the reasons that underlie their success remain an active area of research. We build upon the e
Externí odkaz:
http://arxiv.org/abs/2206.10566
This paper builds upon the work of Pfau (2013), which generalized the bias variance tradeoff to any Bregman divergence loss function. Pfau (2013) showed that for Bregman divergences, the bias and variances are defined with respect to a central label,
Externí odkaz:
http://arxiv.org/abs/2202.04167
Autor:
Allingham, James Urquhart, Wenzel, Florian, Mariet, Zelda E, Mustafa, Basil, Puigcerver, Joan, Houlsby, Neil, Jerfel, Ghassen, Fortuin, Vincent, Lakshminarayanan, Balaji, Snoek, Jasper, Tran, Dustin, Ruiz, Carlos Riquelme, Jenatton, Rodolphe
Machine learning models based on the aggregated outputs of submodels, either at the activation or prediction levels, often exhibit strong performance compared to individual models. We study the interplay of two popular classes of such models: ensembl
Externí odkaz:
http://arxiv.org/abs/2110.03360
Autor:
Nado, Zachary, Band, Neil, Collier, Mark, Djolonga, Josip, Dusenberry, Michael W., Farquhar, Sebastian, Feng, Qixuan, Filos, Angelos, Havasi, Marton, Jenatton, Rodolphe, Jerfel, Ghassen, Liu, Jeremiah, Mariet, Zelda, Nixon, Jeremy, Padhy, Shreyas, Ren, Jie, Rudner, Tim G. J., Sbahi, Faris, Wen, Yeming, Wenzel, Florian, Murphy, Kevin, Sculley, D., Lakshminarayanan, Balaji, Snoek, Jasper, Gal, Yarin, Tran, Dustin
High-quality estimates of uncertainty and robustness are crucial for numerous real-world applications, especially for deep learning which underlies many deployed ML systems. The ability to compare techniques for improving these estimates is therefore
Externí odkaz:
http://arxiv.org/abs/2106.04015
Autor:
Mariet, Zelda Elaine.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 139-150).
This thesis establishes n
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 139-150).
This thesis establishes n
Externí odkaz:
https://hdl.handle.net/1721.1/122739
Autor:
Angermueller, Christof, Belanger, David, Gane, Andreea, Mariet, Zelda, Dohan, David, Murphy, Kevin, Colwell, Lucy, Sculley, D
Publikováno v:
Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020
The use of black-box optimization for the design of new biological sequences is an emerging research area with potentially revolutionary impact. The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental
Externí odkaz:
http://arxiv.org/abs/2006.03227
Many contemporary machine learning models require extensive tuning of hyperparameters to perform well. A variety of methods, such as Bayesian optimization, have been developed to automate and expedite this process. However, tuning remains extremely c
Externí odkaz:
http://arxiv.org/abs/2002.09927