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Anchoring provides a steady start, grounding decisions and perspectives in clarity and confidence.
Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.
Authors:
(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch);
(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch);
(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch).
2 Background
3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure
4 Experiments and 4.1 Linear synthetic data
4.2 Housing nonlinear regression
4.3 In-distribution Generalization
4.4 Out-of-distribution Robustness
5 Conclusion, Broader Impact, and References
A Additional information for Anchor Data Augmentation
Finally, in this section, we present the ADA algorithm step by step (Algorithm 1) to generate minibatches of data that can be used to train neural networks (or any other nonlinear regressor) by any stochastic gradient descent method. As discussed previously, we propose to repeat the augmentation with different parameter combinations for each minibatch.
This paper is available on arxiv under CC0 1.0 DEED license.