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Neighbourhood Components Analysis
In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of theExpand
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context2vec: Learning Generic Context Embedding with Bidirectional LSTM
Context representations are central to various NLP tasks, such as word sense disambiguation, named entity recognition, coreference resolution, and many more. In this work we present a neural modelExpand
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Training deep neural-networks using a noise adaptation layer
The availability of large datsets has enabled neural networks to achieve impressive recognition results. However, the presence of inaccurate class labels is known to deteriorate the performance ofExpand
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Hierarchical Clustering of a Mixture Model
In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original model; this is achievedExpand
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Global Learning of Typed Entailment Rules
Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn aExpand
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An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures
We present two new methods for approximating the Kullback-Liebler (KL) divergence between two mixtures of Gaussians. The first method is based on matching between the Gaussian elements of the twoExpand
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Constrained Gaussian mixture model framework for automatic segmentation of MR brain images
An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used toExpand
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Nonparametric Information Theoretic Clustering Algorithm
In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the data are given inExpand
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Modeling Word Meaning in Context with Substitute Vectors
Context representations are a key element in distributional models of word meaning. In contrast to typical representations based on neighboring words, a recently proposed approach suggests toExpand
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GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Abstract Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scaleExpand
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