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Some contributions to computational Bayesian methods with application to phylolinguistics ; Quelques contributions aux méthodes computationnelles bayesiennes, avec applications à la phylolinguistique
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In: https://tel.archives-ouvertes.fr/tel-03546821 ; Statistics [math.ST]. Université Paris sciences et lettres, 2021. English. ⟨NNT : 2021UPSLD008⟩ (2021)
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Quelques contributions aux méthodes computationnelles bayesiennes, avec applications à la phylolinguistique ; Some contributions to computational Bayesian methods with application to phylolinguistics
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Unsupervised learning of natural language morphology using non-parametric bayesian models ; Apprentissage non-supervisé de la morphologie des langues à l’aide de modèles bayésiens non-paramétriques
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In: https://tel.archives-ouvertes.fr/tel-02354184 ; Informatique et langage [cs.CL]. Université Paris Saclay (COmUE), 2019. Français. ⟨NNT : 2019SACLS203⟩ (2019)
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Build a Bayesian Network from FMECA in the Production of Automotive Parts: Diagnosis and Prediction
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In: ISSN: 2405-8963 ; IFAC-PapersOnLine ; https://hal.archives-ouvertes.fr/hal-03034479 ; IFAC-PapersOnLine, Elsevier, 2019, 52 (13), pp.2572-2577. ⟨10.1016/j.ifacol.2019.11.594⟩ (2019)
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Modelling Dependency Structures Produced by the Introduction of a Flipped Classroom
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In: Mathematics ; Volume 8 ; Issue 1 (2019)
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Probabilistic grammar and constructional predictability: Bayesian generalized additive models of help + (to) Infinitive in varieties of web-based English
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In: Glossa: a journal of general linguistics; Vol 3, No 1 (2018); 55 ; 2397-1835 (2018)
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The interaction of language processing and eye movement control during reading
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In: Abbott, Matthew James Hansen. (2016). The interaction of language processing and eye movement control during reading. UC San Diego: Psychology. Retrieved from: http://www.escholarship.org/uc/item/4052r0s7 (2016)
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PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
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In: https://hal.archives-ouvertes.fr/hal-01134246 ; [Research Report] Université Jean Monnet, Saint-Étienne (42); Département d'Informatique et de Génie Logiciel, Université Laval (Québec); ENS Paris; IST Austria. 2016 (2016)
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Discovering latent structures in syntax trees and mixed-type data
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Inducing grammars from linguistic universals and realistic amounts of supervision
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Statistical Analysis of Online Eye and Face-Tracking Applications in Marketing
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A nonparametric Bayesian perspective for machine learning in partially-observed settings ...
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A nonparametric Bayesian perspective for machine learning in partially-observed settings
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Improved Phrase Translation Modeling Using Maximum A-Posteriori (MAP) Adaptation
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In: DTIC (2013)
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Ein neues Verfahren für namensbasierte Zufallsstichproben von Migranten
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In: Methoden, Daten, Analysen (mda) ; 7 ; 1 ; 5-33 (2013)
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Constructing flexible feature representations using nonparametric Bayesian inference
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In: Austerweil, Joseph Larry. (2012). Constructing flexible feature representations using nonparametric Bayesian inference. UC Berkeley: Psychology. Retrieved from: http://www.escholarship.org/uc/item/7cc9s9hs (2012)
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Abstract:
Representations are a key explanatory device used by cognitive psychologists to account for human behavior. However, little is known about how experience and context affect the representations people use to encode a stimulus. Understanding the effects of context and experience on the representations people use is essential because if two people encode the same stimulus using different representations, their response to that stimulus may be different. First, we present a mathematical framework that can be used to define models that flexibly construct feature representations (where by a feature we mean a part of the image of an object) for a set of observed objects, based on nonparametric Bayesian statistics. An initial model constructed in this framework captures how the distribution of parts and learning categories affects the features people use to represent a set of objects. Next, we build on this work in three ways. First, although people use features that can be transformed on each observation (e.g., translated on the retinal image), many existing feature learning models can only recognize features that are not transformed (occur identically each time). Consequently, we extend the initial model to infer features that are invariant over a set of transformations, and learn different structures of dependence between feature transformations. Second, we compare two possible methods for capturing the manner that categorization affects feature representations. Third, we present a model that learns features incrementally, capturing an effect of the order of object presentation on the features people learn. Finally, we conclude by considering the implications and limitations of our empirical and theoretical results.
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Keyword:
Artificial intelligence; Bayesian modeling; Cognitive psychology; Computational cognitive science; Feature representations; Learning; Nonparametric Bayesian statistics; Perception; Psychology
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URL: http://n2t.net/ark:/13030/m5tb4bfr
http://www.escholarship.org/uc/item/7cc9s9hs
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Constructing flexible feature representations using nonparametric Bayesian inference
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Exemplar Models as a Mechanism for Performing Bayesian Inference
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In: DTIC (2010)
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