Oksana Yakhnenko

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We consider the problem of embedding entities and relationships of multirelational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as(More)
This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on scoring functions that operate by learning low-dimensional embeddings of words, entities and relationships from a knowledge base. We empirically show on New York Times(More)
We consider the problem of embedding entities and relations of knowledge bases in lowdimensional vector spaces. Unlike most existing approaches, which are primarily efficient for modeling equivalence relations, our approach is designed to explicitly model irreflexive relations, such as hierarchies, by interpreting them as translations operating on the(More)
Image annotation is a challenging task that allows to correlate text keywords with an image. In this paper we address the problem of image annotation using Kernel Multiple Linear Regression model. Multiple Linear Regression (MLR) model reconstructs image caption from an image by performing a linear transformation of an image into some semantic space, and(More)
Multiple Instance Multiple Label learning problem has received much attention in machine learning and computer vision literature due to its applications in image classification and object detection. However, the current state-of-the-art solutions to this problem lack scalability and cannot be applied to datasets with a large number of instances and a large(More)
In this paper, we propose a discriminative counterpart of the directed Markov Models of order k - 1, or MM(k - 1) for sequence classification. MM(k - 1) models capture dependencies among neighboring elements of a sequence. The parameters of the classifiers are initialized to based on the maximum likelihood estimates for their generative counterparts. We(More)
Many applications call for learning to label individual objects in an image where the only information available to the learner is a dataset of images with their associated captions, i.e., words that describe the image content without specifically labeling the individual objects. We address this problem using a multi-modal hierarchical Dirichlet process(More)
Image annotation is a challenging task of assigning keywords to an image given the content of an image. It has a variety of applications in multi-media data-mining and computer vision. Traditional machine learning approaches to image annotation require large amounts of labeled data. This requirement is often unrealistic, as obtaining labeled data is, in(More)
Many real-world applications call for learning predictive relationships from multi-modal data. In particular, in multi-media and web applications, given a dataset of images and their associated captions, one might want to construct a predictive model that not only predicts a caption for the image but also labels the individual objects in the image. We(More)