-------- Weitergeleitete Nachricht -------- Betreff: [computational.science] Workshop on Deep Learning Datum: Thu, 23 Feb 2017 17:00:46 +0530 Von: sara@dirf.org An: humanist@Princeton.EDU, agents@cs.umbc.edu, comp.ai.neural-nets@googlegroups.com, www-ws@w3.org, mailman-owner@uni-koblenz.de, www-zig@w3.org, corpora@lists.uib.no, mnasis-l@asis.org, comp.ai.fuzzy@googlegroups.com, eurchap@asis.org, protege-owl@mailman.Stanford.EDU, CORPORA@HD.UIB.NO, eii-associate@lists.eii.edu.au, swi-prolog@iai.uni-bonn.de, EVENTSNOTIFICATION-ANNOUNCE-L@LISTSERV.LSE.AC.UK, w3c-wai-ig@w3.org, sighfis-l@asis.org, ruleml-all@ruleml.org, web4lib@webjunction.org, SUPPORT-VECTOR-MACHINES@jiscmail.ac.uk, novatica@ati.es, www-talk@w3.org, bayes-news@stat.cmu.edu, dl@dl.kr.org, forum-flogic@projects.semwebcentral.org, public-rif-dev@w3.org, sigmed-l@asis.org, sigkm-l@asis.org, asis-l@asis.org, sigtis-l@asis.org, wi@aifb.uni-karlsruhe.de, aib-l@list.msu.edu, pellet-users@lists.owldl.com, sighci-l@asis.org, seworld@cs.colorado.edu, kweb-all@lists.deri.org, www-rdf-logic@w3.org, public-owl-dev@w3.org, ontoweb-list@srvgal01.deri.ie, alife-announce@lists.idyll.org, GRAD_STUDENTS-L@LISTSERV.UIUC.EDU, elsnet-list@elsnet.org, www-international@w3.org, sigifp-l@asis.org, seweb-list@lists.sti2.at, chapter_mi-l@asis.org, sigvis-l@asis.org, SIGMM@pi4.informatik.uni-mannheim.de, seweb-list@srvgal01.deri.ie, drools-research@redhat.com, imageworld@itu.dk, sigdl-l@asis.org, seweb-list@lists.deri.org, sigcr-l@asis.org, siglan-l@asis.org, siguse-l@asis.org, ia-academic@asis.org, comp.ai.genetic@googlegroups.com, dsinfo@mailbox.gsu.edu, ifip-tc11@lists.iaik.tugraz.at, siks@cs.uu.nl, pvc@asis.org, computational.science@lists.iccsa.org, aco-list@iridia.ulb.ac.be, mlnet@ais.fraunhofer.de, kaw@science.uva.nl, MOBIlearn_sig@uni-koblenz.de, SIMSOC@JISCMAIL.AC.UK, DAI-List@mcc.com, comp.ai.alife@googlegroups.com, sigpub-l@asis.org, JESSE@listserv.utk.edu, membres@afihm.org, event@in.tu-clausthal.de, www-ws-desc@w3.org, gascheduling@jiscmail.ac.uk, AISWorld' aisworld@lists.aisnet.org, BUSINESS-PROCESS-MANAGEMENT@JISCMAIL.AC.UK BUSINESS-PROCESS-MANAGEMENT@jiscmail.ac.uk, BUSINESS-RESEARCH@JISCMAIL.AC.UK BUSINESS-RESEARCH@jiscmail.ac.uk, 'Council of Professors conference listserv' cphc-conf@jiscmail.ac.uk, eThesis@topica.com, ITEVA@list.rug.nl, QUALITATIVE-RESEARCH@JISCMAIL.AC.UK QUALITATIVE-RESEARCH@jiscmail.ac.uk, UKAIS-NOTICES@JISCMAIL.AC.UK UKAIS-NOTICES@jiscmail.ac.uk
Deep Learning Applications (Co-located with the Seventh International Conference on Innovative Computing Technology (INTECH 2017) Luton, UK August 16-18, 2017 (www.dirf.org/intech)
Deep Learning (DL) is an important component of computational intelligence which has the core domain machine learning research in it. It provides more efficient algorithms to deal with large-scale data in neuroscience, computer vision, speech recognition, language processing, biomedical informatics, recommender systems, learning theory, robotics, games, and so on. DL is gaining applications in many domains due to the availability of large amount of data coupled with machine learning algorithms. As the DL applications are on increasing trend a workshop on it will enable to identify the emerging trends in the domain.
The proposed workshop will address the below listed but not limited themes.
Neural network architectures DL Applications to the Natural Sciences Visual Perception using Deep Convolutional Neural Networks Deep Learning for Computer Vision Deep Sequence Modeling: Historical Perspective and Current Trends Automatic Terminology Extraction Deep Learning of Behaviors Probabilistic Graphical Models Algorithms Deep Learning for Natural Language Processing Deep Learning Applications at the Enterprise Scale Multi-modal Deep Learning Deep Learning Security Neural Networks From Statistical Decision Theory and Deep Neural Networks Machine Learning and Deep Neural Networks Cognitive Architectures for Object Recognition in Video Learning Representations for Vision, Speech and Text Processing Applications Deep Learning in the Brain Deep Learning for Sequences Interpretable Deep Learning Models for Healthcare Applications Deep Learning for Video Games Data Processing Methods, and Applications of Least Squares Support Vector Machines Deep Generative Models and Unsupervised Learning Natural Language Understanding
Submissions
Submissions should provide original and unpublished research results or ongoing research with simulations. The papers should be between 6 to 8 pages total in length in the IEEE format.
* All the accepted papers will appear in the proceedings published by IEEE and fully indexed by IEEE Xplore.
* Modified version of the selected papers will appear in the special issues of many peer reviewed and indexed journals.
Important Dates
Submission of papers: June 01, 2017 Notification of Acceptance/Rejection: July 01, 2017 Camera Ready: August 01, 2017 Registration August 01, 2017 Conference: August 16-18, 2017
Organizers
Ricardo Rodriguez Jorge, Engineering and Technology Institute, Mexico
Submissions at-http://www.dirf.org/intech/paper-submission/
Contact- intech@dirf.org -------------------------------------------------------------------------
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