1,077 research outputs found
Label-Dependencies Aware Recurrent Neural Networks
In the last few years, Recurrent Neural Networks (RNNs) have proved effective
on several NLP tasks. Despite such great success, their ability to model
\emph{sequence labeling} is still limited. This lead research toward solutions
where RNNs are combined with models which already proved effective in this
domain, such as CRFs. In this work we propose a solution far simpler but very
effective: an evolution of the simple Jordan RNN, where labels are re-injected
as input into the network, and converted into embeddings, in the same way as
words. We compare this RNN variant to all the other RNN models, Elman and
Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language
Understanding (SLU). Thanks to label embeddings and their combination at the
hidden layer, the proposed variant, which uses more parameters than Elman and
Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other
RNNs, but also outperforms sophisticated CRF models.Comment: 22 pages, 3 figures. Accepted at CICling 2017 conference. Best
Verifiability, Reproducibility, and Working Description awar
Comparing Deep Recurrent Networks Based on the MAE Random Sampling, a First Approach
Recurrent neural networks have demonstrated to be good at tackling prediction problems, however due to their high sensitivity to
hyper-parameter configuration, finding an appropriate network is a tough task. Automatic hyper-parameter optimization methods have emerged to find the most suitable configuration to a given problem, but these methods are not generally adopted because of their high computational cost. Therefore, in this study we extend the MAE random sampling, a low-cost method to compare single-hidden layer architectures, to multiple-hidden-layer ones. We validate empirically our proposal and show that it is possible to predict and compare the expected performance of an hyper-parameter configuration in a low-cost way.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
This research was partially funded by Ministerio de Economı́a, Industria y Competitividad, Gobierno de España, and European Regional Development Fund grant numbers TIN2016-81766-REDT (http://cirti.es) and TIN2017-88213-R (http://6city.lcc.uma.es)
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains,
we are regularly confronted with material whose largely unknown thematic
structure we want to uncover. With unsupervised, exploratory analysis, no prior
knowledge about the content is required and highly open-ended tasks can be
supported. In the past few years, probabilistic topic modeling has emerged as a
popular approach to this problem. Nevertheless, the representation of the
latent topics as aggregations of semi-coherent terms limits their
interpretability and level of detail.
This paper presents an alternative approach to topic modeling that maps
topics as a network for exploration, based on distributional semantics using
learned word vectors. From the granular level of terms and their semantic
similarity relations global topic structures emerge as clustered regions and
gradients of concepts. Moreover, the paper discusses the visual interactive
representation of the topic map, which plays an important role in supporting
its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent
Data Analysis (IDA 2015
Zero-Shot Learning by Convex Combination of Semantic Embeddings
Several recent publications have proposed methods for mapping images into
continuous semantic embedding spaces. In some cases the embedding space is
trained jointly with the image transformation. In other cases the semantic
embedding space is established by an independent natural language processing
task, and then the image transformation into that space is learned in a second
stage. Proponents of these image embedding systems have stressed their
advantages over the traditional \nway{} classification framing of image
understanding, particularly in terms of the promise for zero-shot learning --
the ability to correctly annotate images of previously unseen object
categories. In this paper, we propose a simple method for constructing an image
embedding system from any existing \nway{} image classifier and a semantic word
embedding model, which contains the \n class labels in its vocabulary. Our
method maps images into the semantic embedding space via convex combination of
the class label embedding vectors, and requires no additional training. We show
that this simple and direct method confers many of the advantages associated
with more complex image embedding schemes, and indeed outperforms state of the
art methods on the ImageNet zero-shot learning task
View and Illumination Invariant Object Classification Based on 3D Color Histogram Using Convolutional Neural Networks
Object classification is an important step in visual recognition and semantic analysis of visual content. In this paper, we propose a method for classification of objects that is invariant to illumination color, illumination direction and viewpoint based on 3D color histogram. A 3D color histogram of an image is represented as a 2D image, to capture the color composition while preserving the neighborhood information of color bins, to realize the necessary visual cues for classification of objects. Also, the ability of convolutional neural network (CNN) to learn invariant visual patterns is exploited for object classification. The efficacy of the proposed method is demonstrated on Amsterdam Library of Object Images (ALOI) dataset captured under various illumination conditions and angles-of-view
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