Neighbourhood Components Analysis
- J. Goldberger, S. Roweis, Geoffrey E. Hinton, R. Salakhutdinov
- Computer ScienceNIPS
- 1 December 2004
A novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm that directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.
context2vec: Learning Generic Context Embedding with Bidirectional LSTM
- Oren Melamud, J. Goldberger, Ido Dagan
- Computer ScienceConference on Computational Natural Language…
- 1 August 2016
This work presents a neural model for efficiently learning a generic context embedding function from large corpora, using bidirectional LSTM, and suggests they could be useful in a wide variety of NLP tasks.
Training deep neural-networks using a noise adaptation layer
- J. Goldberger, E. Ben-Reuven
- Computer ScienceInternational Conference on Learning…
- 4 November 2016
This study presents a neural-network approach that optimizes the same likelihood function as optimized by the EM algorithm but extended to the case where the noisy labels are dependent on the features in addition to the correct labels.
An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures
- J. Goldberger, S. Gordon, H. Greenspan
- Computer ScienceProceedings Ninth IEEE International Conference…
- 13 October 2003
Two new methods for approximating the Kullback-Liebler (KL) divergence between two mixtures of Gaussians based on matching between the Gaussian elements of the two Gaussian mixture densities are presented.
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
- Maayan Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, H. Greenspan
- Computer ScienceNeurocomputing
- 3 March 2018
Hierarchical Clustering of a Mixture Model
- J. Goldberger, S. Roweis
- Computer ScienceNIPS
- 1 December 2004
An efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original model is proposed by clustering the components by avoiding the need for explicit resampling of datapoints.
Precise Detection in Densely Packed Scenes
- Eran Goldman, Roei Herzig, Tal Hassner
- Computer ScienceComputer Vision and Pattern Recognition
- 1 April 2019
This work proposes a novel, deep-learning based method for precise object detection, designed for such challenging settings as packed retail environments, and shows the method to outperform existing state-of-the-art with substantial margins.
Global Learning of Typed Entailment Rules
- Jonathan Berant, Ido Dagan, J. Goldberger
- Computer ScienceAnnual Meeting of the Association for…
- 19 June 2011
The results show that using global transitivity information substantially improves performance over this resource and several baselines, and that the scaling methods allow us to increase the scope of global learning of entailment-rule graphs.
Modeling Word Meaning in Context with Substitute Vectors
- Oren Melamud, Ido Dagan, J. Goldberger
- Computer ScienceNorth American Chapter of the Association for…
- 2015
A variant of substitute vectors is proposed, which is particularly suitable for measuring context similarity and a novel model for representing word meaning in context based on this context representation, which outperforms state-of-the-art results on lexical substitution tasks in an unsupervised setting.
Constrained Gaussian mixture model framework for automatic segmentation of MR brain images
- H. Greenspan, A. Ruf, J. Goldberger
- Computer ScienceIEEE Transactions on Medical Imaging
- 21 August 2006
An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented and the applicability of the framework can be extended to diseased brains and neonatal brains.
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