962 research outputs found
Reference map of Adelaide and suburbs [cartographic material] /
No full textCover title: W.G. Fullers reference map of Adelaide and suburbs.; Street map of Adelaide showing railways, tramways and concentric circles showing distance from the GPO.; Rex Nan Kivell Collection Map NK 6047.W.G. Fullers reference map of Adelaide and suburb
The Opinion - Vol. 14, No. 04
No full textOriginally published in print for Fuller Theological Seminary\u27s community from 1962 through 1977.https://digitalcommons.fuller.edu/fts-opinion/1153/thumbnail.jp
The Opinion - Vol. 14, No. 02
No full textOriginally published in print for Fuller Theological Seminary\u27s community from 1962 through 1977.https://digitalcommons.fuller.edu/fts-opinion/1152/thumbnail.jp
The Opinion -
No full textOriginally published in print for Fuller Theological Seminary\u27s community from 1962 through 1977.https://digitalcommons.fuller.edu/fts-opinion/1163/thumbnail.jp
Rex Fuller Interview 2017
No full textIn a short interview, Rex Fuller describes his experiences in the university system both during his time as University President at WOU, and before at other universities
[Portrait of Rex Fuller 1]
No full textUndated portrait of Rex Fuller, who served on the Texas Tech Board of Regents from 1981-1993
2021 Academic Excellence Showcase TRIO Welcome Video
No full textA brief welcome message to WOU\u27s 2020 Academic Excellence Showcase TRIO student participants from WOU President Rex Fuller
2021 Academic Excellence Showcase Welcome Video
No full textA brief welcome message to WOU\u27s 2021 Academic Excellence Showcase participants from WOU President Rex Fuller
2020 Academic Excellence Showcase Welcome Video
No full textA brief welcome message to WOU\u27s 2020 Academic Excellence Showcase participants from WOU President Rex Fuller and WOU Provost Rob Winningham
Understanding Risk Extrapolation (REx) and when it finds Invariant Relationships
No full textGeneralizing models for new unknown datasets is a common problem in machine learning. Algorithms that perform well for test instances with the same distribution as their training dataset often perform severely on new datasets with a different distribution. This problem is caused by distributional shifts between the training of the model and applying that model to a test domain. This paper addresses whether and in what situations Risk Extrapolation (REx) can tackle this problem of Out-Of-Distribution generalization by exploiting invariant relationships. These relationships are based on features that are invariant across all domains. By learning these relationships, REx aims to learn the concept of the problem we are trying to solve. We show in what situations REx can learn these invariant relationships and when it does not. We translate the definition of an invariant relationship into a homoscedastic synthetic dataset with either covariate, confounded, anti-causal, or hybrid shift. We expose REx to experiments in sample complexity, the number of training domains, and the training domain distance. We show that REx performs better for invariant prediction in situations with larger sample sizes and training domain distance and that if these criteria are met, REx performs equivalently in all four distributional shifts. We also compare REx to Invariant- and Empirical Risk Minimization and show that; REx is less sensitive and thus robust to the shifting of the average distributional variance in the training domains; REx asymptotically out-performs the methods in the more complex distributional shifts.https://gitlab.com/hofland.jeroen/rex-distributional-shift CodeCSE3000 Research ProjectComputer Science and Engineerin
- …
