2 research outputs found
GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks
The Fisher information metric is an important foundation of information
geometry, wherein it allows us to approximate the local geometry of a
probability distribution. Recurrent neural networks such as the
Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield
state-of-the-art performance on speech translation or image captioning have so
far ignored the geometry of the latent embedding, that they iteratively learn.
We propose the information geometric Seq2Seq (GeoSeq2Seq) network which
abridges the gap between deep recurrent neural networks and information
geometry. Specifically, the latent embedding offered by a recurrent network is
encoded as a Fisher kernel of a parametric Gaussian Mixture Model, a formalism
common in computer vision. We utilise such a network to predict the shortest
routes between two nodes of a graph by learning the adjacency matrix using the
GeoSeq2Seq formalism; our results show that for such a problem the
probabilistic representation of the latent embedding supersedes the
non-probabilistic embedding by 10-15\%
Algorithmic clothing: hybrid recommendation, from street-style-to-shop
In this paper we detail Cortexica's (https://www.cortexica.com)
recommendation framework -- particularly, we describe how a hybrid visual
recommender system can be created by combining conditional random fields for
segmentation and deep neural networks for object localisation and feature
representation. The recommendation system that is built after localisation,
segmentation and classification has two properties -- first, it is knowledge
based in the sense that it learns pairwise preference/occurrence matrix by
utilising knowledge from experts (images from fashion blogs) and second, it is
content-based as it utilises a deep learning based framework for learning
feature representation. Such a construct is especially useful when there is a
scarcity of user preference data, that forms the foundation of many
collaborative recommendation algorithms.Comment: KDD 2017 Workshop on ML meets Fashio