Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ...
Publications
5 shownPANDA: Pose Aligned Networks for Deep Attribute Modeling
We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose...
Efficient Learning of Sparse Representations with an Energy-Based Model
We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity...
Large Scale Distributed Deep Networks
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider t...
Building high-level features using large scale unsupervised learning
We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabe...
Frequent Co-Authors
Researcher Info
- h-index
- 5
- Publications
- 5
- Citations
- 5,853
- Institution
- Meta (Israel)
External Links
Impact Metrics
h-index
5
h-index: Number of publications with at least h citations each.