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From Recurrent Neural Network to Long Short Term Memory Architecture -- Application to Handwriting Recognition
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In: http://hal.inria.fr/docs/00/86/10/63/PDF/_WEI_XiaoXin_Internship_Reports_final.pdf (2013)
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Abstract:
Despite more than 30 years of handwriting recognition research, Recognizing the unconstrained sequence is still a challenge task. The difficulty of segmenting cursive script has led to the low recognition rate. Hidden Markov Models (HMMs) are considered as state-of-theart methods for performing non-constrained handwriting recognition. However, HMMs have several well-known drawbacks. One of these is that they assume the probability of each observation depends only on the current state, which makes contextual effects difficult to model. Another is that HMMs are generative, while discriminative models generally give better performance in labelling and classification tasks. Recurrent neural networks (RNNs) do not suffer from these limitations, and would therefore seem a promising alternative to HMMs. A novel type of recurrent neural network, termed as Bidirectional
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Keyword:
Lexicon-based; Long short term memory; Neural network; Sequence modeling
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URL: http://hal.inria.fr/docs/00/86/10/63/PDF/_WEI_XiaoXin_Internship_Reports_final.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.411.6768
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LSTM-modeling of continuous emotions in an audiovisual affect recognition framework
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In: http://www.mmk.ei.tum.de/publ//pdf/12/12woe2.pdf (2012)
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Encoding, rehearsal, and recall in signers and speakers: shared network but differential engagement
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In: http://cercor.oxfordjournals.org/content/18/10/2263.full.pdf (2008)
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Exploring dyslexics' phonological deficit I: Lexical vs. sub-lexical and input vs. output processes
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In: http://www.lscp.net/persons/ramus/docs/Dyslexia05.pdf (2005)
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| 5 |
7 p d
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In: https://perswww.kuleuven.be/%7Eu0032380/papers/Boets_BL_2006.pdf (2005)
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| 6 |
Journal of Memory and Language Journal of
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In: http://web.missouri.edu/~cowann/docs/articles/2004/Jarrold et al JML 2004.pdf (2004)
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| 7 |
Named Entity Recognition with Long Short-Term Memory
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In: http://cnts.uia.ac.be/conll2003/ps/17275ham.ps.gz (2003)
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| 8 |
Individual differences in semantic short-term memory capacity and reading comprehension
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In: http://www.ingilizceveingilizce.com/ingilizce/Individual differences in semantic short-term memory capacit.pdf (2003)
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| 9 |
y r a
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In: http://ivrylab.berkeley.edu/uploads/4/1/1/5/41152143/justus_et_al._-_2005_-_brain_and_language.pdf
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| 10 |
Efficient Exploration in Reinforcement Learning Based on Short-term Memory
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In: http://www.cs.rtu.lv/dssg/download/publications/2002/Pchelkin-Denmark-2002.pdf
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| 11 |
Phonological Similarity Effects Without a Phonological Store: An Individual Differences Model
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In: http://csjarchive.cogsci.rpi.edu/Proceedings/2007/docs/p89.pdf
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| 12 |
Is there brief temporal buffering of successive visual inputs?
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In: http://hannahsmithson.org/research-papers/SmithMollonBhardwajSmithson_2011_BriefTemporalBuffering.pdf
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| 13 |
e i
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In: http://www.psychology.pitt.edu/research/publications/becker_functional_dissociati.pdf
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| 14 |
e i
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In: http://www.psychology.pitt.edu/research/publications/fiez_functional_dissociati.pdf
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| 15 |
On the division of short-term and working memory: An examination of simple and complex spans and their relation to higher-order abilities
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In: http://psychology.uga.edu/maidlab/Pubs/Unsworth%26Engle(2007b).pdf
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| 16 |
AUDIOVISUAL VOCAL OUTBURST RECOGNITION IN NOISY ACOUSTIC CONDITIONS
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In: http://www.mmk.ei.tum.de/publ//pdf/12/12eyb1.pdf
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Audiovisual classification of vocal outburst in human conversation using long–short-term memory networks
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In: http://ibug.doc.ic.ac.uk/media/uploads/documents/icassp-2011-eybenetal-final.pdf
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| 18 |
AUDIOVISUAL CLASSIFICATION OF VOCAL OUTBURSTS IN HUMAN CONVERSATION USING LONG-SHORT-TERM MEMORY NETWORKS
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In: http://www.mmk.ei.tum.de/publ/pdf/11/11eyb2.pdf
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| 19 |
AUDIOVISUAL VOCAL OUTBURST CLASSIFICATION IN NOISY ACOUSTIC CONDITIONS
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In: http://ibug.doc.ic.ac.uk/media/uploads/documents/eybenetal_icassp2012.pdf
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| 20 |
Sex-based Short-term Memory of Vocabulary Trained with Pease and Pease‟s Multimodality
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In: http://www.macrothink.org/journal/index.php/ijhrs/article/viewFile/2730/2670/
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