Unsupervised feature extraction with autoencoder trees

By Ozan Irsoy, Ethem Alpaydin. 2017

59 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Generative Models, Representation & Self-Supervised Learning, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xin He
2. Kaiyong Zhao
3. Xiaowen Chu
4. Marco F. Huber
5. Marc-André Zöller

In Computer Vision and Pattern Recognition

1. Xin He
2. Kaiyong Zhao
3. Xiaowen Chu

In Generative Models

1. Xin He
2. Kaiyong Zhao
3. Xiaowen Chu

In Representation & Self-Supervised Learning

1. Xin He
2. Kaiyong Zhao
3. Xiaowen Chu

In Signal Processing

1. Xin He
2. Kaiyong Zhao
3. Xiaowen Chu

Citations in the corpus

• AutoML: A Survey of the State-of-the-Art (2019, methodology): “model that considers data features and their relationships, while an unsupervised feature-extraction method using autoencoder trees is proposed by [91].”