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This work introduces a model that can recognize objects in images even if no training data is available for the objects.
A solution to Plato’s problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge
T. K. Landauer and S. T. Dumais · 1997
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Automatic retrieval and clustering of similar words
D. Lin · 1998
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Automatic word sense discrimination
H. Schütze · 1998
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A neural probabilistic language model
Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin · 2003
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From Distributional to Semantic Similarity
J. Curran · 2004
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Object classification from a single example utilizing class relevance pseudo-metrics
M. Fink · 2004
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Cross-generalization: learning novel classes from a single example by feature replacement
E. Bart and S. Ullman · 2005
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Geometric context from a single image
D. Hoiem, A.A. Efros, and M. Herbert · 2005
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One-shot learning of object categories
R.; Perona L. Fei-Fei; Fergus · 2006
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Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
J. Blitzer, M. Dredze, and F. Pereira · 2007
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Dependency-based construction of semantic space models
S. Pado and M. Lapata · 2007
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A unified architecture for natural language processing: deep neural networks with multitask learning
R. Collobert and J. Weston · 2008
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A structured vector space model for word meaning in context
K. Erk and S. Padó · 2008
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Zero-data learning of new tasks
H. Larochelle, D. Erhan, and Y. Bengio · 2008
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L. van der Maaten and G. Hinton · 2008
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Describing objects by their attributes
A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth · 2009
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LoOP: local Outlier Probabilities
H. Kriegel, P. Kröger, E. Schubert, and A. Zimek · 2009
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From frequency to meaning: Vector space models of semantics
P. D. Turney and P. Pantel · 2010
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The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization
A. Coates and A. Ng · 2011
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Domain adaptation for Large-Scale sentiment classification: A deep learning approach
X. Glorot, A. Bordes, and Y. Bengio · 2011
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One shot learning of simple visual concepts
B. M. Lake, J. Gross R. Salakhutdinov, and J. B. Tenenbaum · 2011
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Going beyond text: A hybrid image-text approach for measuring word relatedness
C.W. Leong and R. Mihalcea · 2011
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Multimodal deep learning
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A.Y. Ng · 2011
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Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer
C. H. Lampert, H. Nickisch, and S. Harmeling · 2009
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Zero-shot learning with semantic output codes
M. Palatucci, D. Pomerleau, G. Hinton, and T. Mitchell · 2009
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Distributional memory: A general framework for corpus-based semantics
M. Baroni and A. Lenci · 2010
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Visual information in semantic representation
Y. Feng and M. Lapata · 2010
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Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora
R. Socher and L. Fei-Fei · 2010
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Towards cross-category knowledge propagation for learning visual concepts
Guo-Jun Qi, C. Aggarwal, Y. Rui, Q. Tian, S. Chang, and T. Huang · 2011
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Distributional semantics in technicolor
E. Bruni, G. Boleda, M. Baroni, and N. Tran · 2012
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Improving Word Representations via Global Context and Multiple Word Prototypes
E. H. Huang, R. Socher, C. D. Manning, and A. Y. Ng · 2012
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Learning to learn with compound hierarchical-deep models
A. Torralba R. Salakhutdinov, J. Tenenbaum · 2012
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Multimodal learning with deep boltzmann machines
N. Srivastava and R. Salakhutdinov · 2012
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