24,527 research outputs found
PhotoRaptor - Photometric Research Application To Redshifts
Due to the necessity to evaluate photo-z for a variety of huge sky survey
data sets, it seemed important to provide the astronomical community with an
instrument able to fill this gap. Besides the problem of moving massive data
sets over the network, another critical point is that a great part of
astronomical data is stored in private archives that are not fully accessible
on line. So, in order to evaluate photo-z it is needed a desktop application
that can be downloaded and used by everyone locally, i.e. on his own personal
computer or more in general within the local intranet hosted by a data center.
The name chosen for the application is PhotoRApToR, i.e. Photometric Research
Application To Redshift (Cavuoti et al. 2015, 2014; Brescia 2014b). It embeds a
machine learning algorithm and special tools dedicated to preand
post-processing data. The ML model is the MLPQNA (Multi Layer Perceptron
trained by the Quasi Newton Algorithm), which has been revealed particularly
powerful for the photo-z calculation on the base of a spectroscopic sample
(Cavuoti et al. 2012; Brescia et al. 2013, 2014a; Biviano et al. 2013).
The PhotoRApToR program package is available, for different platforms, at the
official website (http://dame.dsf.unina.it/dame_photoz.html#photoraptor).Comment: User Manual of the PhotoRaptor tool, 54 pages. arXiv admin note:
substantial text overlap with arXiv:1501.0650
An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog
We present a novel end-to-end trainable neural network model for
task-oriented dialog systems. The model is able to track dialog state, issue
API calls to knowledge base (KB), and incorporate structured KB query results
into system responses to successfully complete task-oriented dialogs. The
proposed model produces well-structured system responses by jointly learning
belief tracking and KB result processing conditioning on the dialog history. We
evaluate the model in a restaurant search domain using a dataset that is
converted from the second Dialog State Tracking Challenge (DSTC2) corpus.
Experiment results show that the proposed model can robustly track dialog state
given the dialog history. Moreover, our model demonstrates promising results in
producing appropriate system responses, outperforming prior end-to-end
trainable neural network models using per-response accuracy evaluation metrics.Comment: Published at Interspeech 201
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