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Hits 1 – 20 of 45
1
Iterated learning of multiple languages from multiple teachers
David Burkett
;
Thomas L. Griffiths
In: http://www.cs.berkeley.edu/%7Edburkett/papers/burkett10-iterated_multilingual.pdf (2010)
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2
The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning.
Florencia Reali
;
Thomas L Griffiths
In: http://csjarchive.cogsci.rpi.edu/Proceedings/2008/pdfs/p229.pdf (2009)
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3
Learning phonetic categories by learning a lexicon.
Naomi H Feldman
;
Thomas L Griffiths
;
James L Morgan
In: http://www.socsci.uci.edu/%7Elpearl/colareadinggroup/readings/FeldmanGriffithsMorgan2009_LexiconSegmentationBayesian.pdf (2009)
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4
The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference
Naomi H. Feldman
;
Thomas L. Griffiths
;
James L. Morgan
...
In: http://ling.umd.edu/%7Enhf/papers/PerceptualMagnetLong.pdf (2009)
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5
The influence of categories on perception: explaining the perceptual magnet effect as optimal statistical inference
Naomi H. Feldman
;
Thomas L. Griffiths
;
James L. Morgan
In: http://cocosci.berkeley.edu/tom/papers/magnet.pdf (2009)
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6
A Bayesian Framework for Word Segmentation: Exploring the Effects of Context
Sharon Goldwater
;
Thomas L. Griffiths
;
Mark Johnson
In: http://www.cog.brown.edu/~mj/papers/GoldwaterGriffithsJohnson09WordSegmentation.pdf (2009)
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7
A bayesian framework for word segmentation: Exploring the effects of context
Sharon Goldwater
;
Thomas L. Griffiths
;
Mark Johnson
In: http://homepages.inf.ed.ac.uk/sgwater/papers/cognition-hdp.pdf (2009)
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8
Differencial use of implicit negative evidence in generative and discriminative language learning
Anne S. Hsu
;
Thomas L. Griffiths
In: http://cocosci.berkeley.edu/tom/papers/gendisclang.pdf (2009)
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9
Differencial use of implicit negative evidence in generative and discriminative language learning
Anne S. Hsu
;
Thomas L. Griffiths
In: http://books.nips.cc/papers/files/nips22/NIPS2009_0639.pdf (2009)
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10
Performing Bayesian inference with exemplar models
Lei Shi
;
Naomi H. Feldman (naomi
;
Thomas L. Griffiths (tom
In: http://ling.umd.edu/%7Enhf/papers/Exemplar.pdf (2008)
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11
Performing Bayesian inference with exemplar models
Lei Shi
;
Naomi H. Feldman (naomi
;
Thomas L. Griffiths (tom
In: http://cocosci.berkeley.edu/tom/papers/exemplar.pdf (2008)
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12
Performing Bayesian inference with exemplar models
Lei Shi
;
Naomi H. Feldman (naomi
;
Thomas L. Griffiths (tom
In: http://csjarchive.cogsci.rpi.edu/Proceedings/2008/pdfs/p745.pdf (2008)
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13
Topics in semantic representation
Thomas L. Griffiths
;
Joshua B. Tenenbaum
;
Mark Steyvers
In: http://psiexp.ss.uci.edu/research/papers/Griffiths_Steyvers_Tenenbaum_2007.pdf (2007)
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14
Modeling human performance on statistical word segmentation tasks
Michael C. Frank
;
Thomas L. Griffiths
;
Joshua B. Tenenbaum
In: http://homepages.inf.ed.ac.uk/sgwater/papers/cognition10-segmem.pdf (2007)
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15
Topics in semantic representation
Thomas L. Griffiths
;
Mark Steyvers
;
Joshua B. Tenenbaum
In: http://psych.stanford.edu/~jlm/pdfs/Griffiths-etal-PR-2007.pdf (2007)
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16
Bayesian inference for PCFGs via Markov chain Monte Carlo
Mark Johnson
;
Thomas L. Griffiths
In: http://acl.ldc.upenn.edu/N/N07/N07-1018.pdf (2007)
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17
Bayesian inference for PCFGs via Markov chain Monte Carlo
Mark Johnson
;
Thomas L. Griffiths
In: http://www.stanford.edu/~sgwater/papers/naacl07-mcmc-pcfg.pdf (2007)
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18
Two proposals for causal grammar
Thomas L. Griffiths
;
Joshua B. Tenenbaum
In: http://web.mit.edu/cocosci/Papers/gt-grammar.pdf (2007)
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19
Topics in semantic representation
Tom Griffiths
;
Thomas L. Griffiths
;
Mark Steyvers
...
In: http://web.mit.edu/cocosci/Papers/topics15.pdf (2007)
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20
A fully bayesian approach to unsupervised part-of-speech tagging
Thomas L. Griffiths
In: http://nlp.stanford.edu/pubs/goldwater_griffiths_acl07.pdf (2007)
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