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Hits 21 – 40 of 145
21
Toward a Psycholinguistically Plausible Parser ...
Macwhinney, Brian
. - : Figshare, 2018
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22
Can Our Experiments Illuminate Reality? ...
Macwhinney, Brian
. - : Figshare, 2018
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23
The emergence of linguistic complexity ...
Macwhinney, Brian
. - : Figshare, 2018
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24
The emergence of linguistic complexity ...
Macwhinney, Brian
. - : Figshare, 2018
BASE
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25
Can Our Experiments Illuminate Reality? ...
Macwhinney, Brian
. - : Figshare, 2018
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26
Are These Approaches Incompatible? ...
Macwhinney, Brian
. - : Carnegie Mellon University, 2017
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27
The Instructed Learning of Form–Function Mappings in the English Article System ...
Zhao, Helen
;
Macwhinney, Brian
. - : Carnegie Mellon University, 2017
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28
The Instructed Learning of Form–Function Mappings in the English Article System ...
Zhao, Helen
;
Macwhinney, Brian
. - : Carnegie Mellon University, 2017
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29
Are These Approaches Incompatible? ...
Macwhinney, Brian
. - : Carnegie Mellon University, 2017
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30
The Impact of Co-Occurrence and Context on the Prediction of Long-Distance Separable Prefixes ...
Walter, Daniel
;
Macwhinney, Brian
. - : Carnegie Mellon University, 2017
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31
The Impact of Co-Occurrence and Context on the Prediction of Long-Distance Separable Prefixes ...
Walter, Daniel
;
Macwhinney, Brian
. - : Carnegie Mellon University, 2017
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32
Fostering Human Rights Through TalkBank ...
Macwhinney, Brian
;
Fromm, Davida
;
Rose, Yvan
. - : Carnegie Mellon University, 2017
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33
Fostering Human Rights Through TalkBank ...
Macwhinney, Brian
;
Fromm, Davida
;
Rose, Yvan
. - : Carnegie Mellon University, 2017
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34
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ...
Tsvetkov, Yulia
;
Manaal Faruqui
;
Ling, Wang
;
Macwhinney, Brian
;
Dyer, Chris
. - : Carnegie Mellon University, 2016
Abstract:
We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features. The curricula are modeled by a linear ranking function which is the scalar product of a learned weight vector and an engineered feature vector that characterizes the different aspects of the complexity of each instance in the training corpus. We show that learning the curriculum improves performance on a variety of downstream tasks over random orders and in comparison to the natural corpus order. ...
Keyword:
170204 Linguistic Processes incl. Speech Production and Comprehension
;
FOS Psychology
URL:
https://kilthub.cmu.edu/articles/Learning_the_Curriculum_with_Bayesian_Optimization_for_Task-Specific_Word_Representation_Learning/5895703
https://dx.doi.org/10.1184/r1/5895703
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35
AphasiaBank as BigData ...
Macwhinney, Brian
;
Fromm, Davida
. - : Carnegie Mellon University, 2016
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36
A Shared Platform for Studying Second Language Acquisition ...
Macwhinney, Brian
. - : Carnegie Mellon University, 2016
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37
A Shared Platform for Studying Second Language Acquisition ...
Macwhinney, Brian
. - : Carnegie Mellon University, 2016
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38
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ...
Tsvetkov, Yulia
;
Manaal Faruqui
;
Ling, Wang
. - : Carnegie Mellon University, 2016
BASE
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39
Automated Proposition Density Analysis for Discourse in Aphasia ...
Fromm, Davida
;
Greenhouse, Joel
;
Kaiyue Hou
. - : Carnegie Mellon University, 2016
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40
CHILDES for Japanese: Corpora, Programs, Perspectives ...
Miyata, Susanne
;
Macwhinney, Brian
. - : Carnegie Mellon University, 2016
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