Abstract
| Language | English |
|---|---|
| Status | Published - 9 Dec 2016 |
| Event | NIPS Workshop on Learning in High Dimensions with Structure - Duration: 9 Dec 2016 → … https://sites.google.com/site/structuredlearning16/ |
Workshop
| Workshop | NIPS Workshop on Learning in High Dimensions with Structure |
|---|---|
| Period | 9/12/16 → … |
| Internet address |
Cite this
Learning Alignments from Latent Space Structures. / Kazlauskaite, Ieva; Ek, Carl Henrik; Campbell, Neill.
2016. Paper presented at NIPS Workshop on Learning in High Dimensions with Structure, .Research output: Contribution to conference › Paper
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TY - CONF
T1 - Learning Alignments from Latent Space Structures
AU - Kazlauskaite, Ieva
AU - Ek, Carl Henrik
AU - Campbell, Neill
PY - 2016/12/9
Y1 - 2016/12/9
N2 - In this paper we present a model that is capable of learning alignments between high-dimensional data by exploiting low-dimensional structures. Specifically, our method uses a Gaussian process latent variable model (GP-LVM) to learn alignments and latent representations simultaneously. The results show that our model performs alignment implicitly and improves the smoothness of the low dimensional representations.
AB - In this paper we present a model that is capable of learning alignments between high-dimensional data by exploiting low-dimensional structures. Specifically, our method uses a Gaussian process latent variable model (GP-LVM) to learn alignments and latent representations simultaneously. The results show that our model performs alignment implicitly and improves the smoothness of the low dimensional representations.
M3 - Paper
ER -