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Classifying data objects

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The article was published on 2014-12-19. It has received 17 citations till now. The article focuses on the topics: Representation term.

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Patent

Deep learning for semantic parsing including semantic utterance classification

TL;DR: In this paper, a semantic parsing mechanism that uses a deep model trained at least in part via unsupervised learning using unlabeled data is presented. But the model is trained from query click log data.
Patent

Systems and methods for extracting information about objects from scene information

TL;DR: In this paper, various methods and systems are described for information extraction from scene information. But, the 2D image information can be combined with 3D information about the scene incorporating at least part of the object(s) to generate projective geometry information.
Patent

Systems and methods for normalizing an image

TL;DR: In this paper, a method for normalizing an image by an electronic device is described, which includes obtaining an image including a target object and determining a set of windows of the image.
Patent

Methods and systems for counting people

TL;DR: In this article, a neural network is used to count the number of people present in an environment, and an output value is updated to indicate the count of people identified as being present in the environment.
Patent

Automated surveying of real world objects

TL;DR: In this article, a method for automatic surveying of a real word object (41,41b,47) by a surveying or metrology instrument (44,44b,46) is described.
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Efficient Estimation of Word Representations in Vector Space

TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Proceedings Article

DeViSE: A Deep Visual-Semantic Embedding Model

TL;DR: This paper presents a new deep visual-semantic embedding model trained to identify visual objects using both labeled image data as well as semantic information gleaned from unannotated text and shows that the semantic information can be exploited to make predictions about tens of thousands of image labels not observed during training.
Proceedings ArticleDOI

Learning to detect unseen object classes by between-class attribute transfer

TL;DR: The experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes, and assembled a new large-scale dataset, “Animals with Attributes”, of over 30,000 animal images that match the 50 classes in Osherson's classic table of how strongly humans associate 85 semantic attributes with animal classes.
Proceedings ArticleDOI

Zero-shot Learning with Semantic Output Codes

TL;DR: A semantic output code classifier which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes and can often predict words that people are thinking about from functional magnetic resonance images of their neural activity, even without training examples for those words.