Aug 1, 2016 · In domain adaptation, the goal is to find common ground between two, potentially differently distributed, data sets.

This manifold encompasses projection matrices of word vectors onto low-dimensional latent feature representations, which allows us to interpret the results: the ...

This work proposes a solution to the domain adaptation task, by efficiently solving an optimization problem through Stochastic Gradient Descent through ...

In domain adaptation, the goal is to find common ground between two, potentially differently distributed, data sets. By finding common concepts present in ...

Abstract: In domain adaptation, the goal is to find common ground between two, potentially differently distributed, data sets. By finding common concepts ...

Interpretable domain adaptation via optimization over the Stiefel manifold. Pölitz, C.; Duivesteijn, W.; Morik, K. Machine Learning 104(2-3): 315-336. ISSN ...

We propose a solution to the domain adaptation task, by efficiently solving an optimization problem through Stochastic Gradient Descent. We provide update rules ...

Interpretable domain adaptation via optimization over the Stiefel manifold. C Pölitz, W Duivesteijn, K Morik. Machine Learning 104, 315-336, 2016. 12, 2016.

Mar 2, 2020 · Christian Pölitz , Wouter Duivesteijn, Katharina Morik: Interpretable domain adaptation via optimization over the Stiefel manifold.

By using RSGD to optimize over the Stiefel manifold, Pölitz et al. (2016) attempts to improve interpretability of domain adaptation and has demonstrated its ...