Results for ' generative models'

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  1.  44
    From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology.Maxwell J. D. Ramstead, Anil K. Seth, Casper Hesp, Lars Sandved-Smith, Jonas Mago, Michael Lifshitz, Giuseppe Pagnoni, Ryan Smith, Guillaume Dumas, Antoine Lutz, Karl Friston & Axel Constant - 2022 - Review of Philosophy and Psychology 13 (4):829-857.
    This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as _computational phenomenology_ because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and (...)
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  2. Calibrating Generative Models: The Probabilistic Chomsky-Schützenberger Hierarchy.Thomas Icard - 2020 - Journal of Mathematical Psychology 95.
    A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using (...)
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  3. Are Generative Models Structural Representations?Marco Facchin - 2021 - Minds and Machines 31 (2):277-303.
    Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine (...)
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  4.  29
    Generative Models.Sim-Hui Tee - 2020 - Erkenntnis 88 (1):23-41.
    Generative models have been proposed as a new type of non-representational scientific models recently. A generative model is characterized with the capacity of producing new models on the basis of the existing one. The current accounts do not explain sufficiently the mechanism of the generative capacity of a generative model. I attempt to accomplish this task in this paper. I outline two antecedent accounts of generative models. I point out that both (...)
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  5.  38
    Generative models as parsimonious descriptions of sensorimotor loops.Manuel Baltieri & Christopher L. Buckley - 2019 - Behavioral and Brain Sciences 42.
    The Bayesian brain hypothesis, predictive processing, and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world.
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  6.  22
    A Generative Model for Semantic Role Labeling.Cynthia A. Thompson, Roger Levy & Christopher D. Manning - unknown
    Determining the semantic role of sentence constituents is a key task in determining sentence meanings lying behind a veneer of variant syntactic expression. We present a model of natural language generation from semantics using the FrameNet semantic role and frame ontology. We train the model using the FrameNet corpus and apply it to the task of automatic semantic role and frame identification, producing results competitive with previous work (about 70% role labeling accuracy). Unlike previous models used for this task, (...)
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  7.  33
    Imaginative Constraints and Generative Models.Daniel Williams - 2021 - Australasian Journal of Philosophy 99 (1):68-82.
    ABSTRACT How can imagination generate knowledge when its contents are voluntarily determined? Several philosophers have recently answered this question by pointing to the constraints that underpin imagination when it plays knowledge-generating roles. Nevertheless, little has been said about the nature of these constraints. In this paper, I argue that the constraints that underpin sensory imagination come from the structure of causal probabilistic generative models, a construct that has been highly influential in recent cognitive science and machine learning. I (...)
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  8.  42
    Imaginative Constraints and Generative Models.Daniel Williams - 2021 - Australasian Journal of Philosophy 99 (1):68-82.
    ABSTRACT How can imagination generate knowledge when its contents are voluntarily determined? Several philosophers have recently answered this question by pointing to the constraints that underpin imagination when it plays knowledge-generating roles. Nevertheless, little has been said about the nature of these constraints. In this paper, I argue that the constraints that underpin sensory imagination come from the structure of causal probabilistic generative models, a construct that has been highly influential in recent cognitive science and machine learning. I (...)
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  9.  12
    A generative model for translating from ordinary language into symbolic notation.William E. McMahon - 1997 - Synthese 35 (1):99-116.
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  10. Dreaming the Whole Cat: Generative Models, Predictive Processing, and the Enactivist Conception of Perceptual Experience.Andy Clark - 2012 - Mind 121 (483):753-771.
    Does the material basis of conscious experience extend beyond the boundaries of the brain and central nervous system? In Clark 2009 I reviewed a number of ‘enactivist’ arguments for such a view and found none of them compelling. Ward (2012) rejects my analysis on the grounds that the enactivist deploys an essentially world-involving concept of experience that transforms the argumentative landscape in a way that makes the enactivist conclusion inescapable. I present an alternative (prediction-and-generative-model-based) account that neatly accommodates all (...)
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  11.  35
    Generative models: Human embryonic stem cells and multiple modeling relations.Melinda Bonnie Fagan - 2016 - Studies in History and Philosophy of Science Part A 56:122-134.
  12.  58
    A generative model for translating from ordinary language into symbolic notation.William E. Mcmahon - 1977 - Synthese 35 (1):99 - 116.
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  13.  39
    A generative model for semantic role labeling.Christopher Manning - manuscript
    Determining the semantic role of sentence constituents is a key task in determining sentence meanings lying behind a veneer of variant syntactic expression. We present a model of natural language generation from semantics using the FrameNet semantic role and frame ontology. We train the model using the FrameNet corpus and apply it to the task of automatic semantic role and frame identification, producing results competitive with previous work (about 70% role labeling accuracy). Unlike previous models used for this task, (...)
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  14.  34
    Classification objects, ideal observers & generative models.Cheryl Olman & Daniel Kersten - 2004 - Cognitive Science 28 (2):227-239.
    A successful vision system must solve the problem of deriving geometrical information about three-dimensional objects from two-dimensional photometric input. The human visual system solves this problem with remarkable efficiency, and one challenge in vision research is to understand howneural representations of objects are formed and what visual information is used to form these representations. Ideal observer analysis has demonstrated the advantages of studying vision from the perspective of explicit generative models and a specified visual task, which divides the (...)
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  15.  17
    A generative model of conversation.Gheorghe Pǎun - 1976 - Semiotica 17 (1).
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  16. Content and misrepresentation in hierarchical generative models.Alex Kiefer & Jakob Hohwy - 2018 - Synthese 195 (6):2387-2415.
    In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing (...)
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  17.  34
    Causal generative models are just a start.Ernest Davis & Gary Marcus - 2017 - Behavioral and Brain Sciences 40.
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  18.  8
    A generative model in architecture.Gabriela Ghioca - 1983 - Semiotica 45 (3-4).
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  19.  15
    The intentional structure of generative models.Majid D. Beni - forthcoming - Phenomenology and the Cognitive Sciences:1-12.
    There are various philosophical interpretations of the account of consciousness associated with the temporal depth of generative models under the Free Energy Principle. This paper strives to develop a new philosophical interpretation of the free energy account of consciousness along the lines of intentionalism.
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  20.  7
    Implications of capacity-limited, generative models for human vision.Joseph Scott German & Robert A. Jacobs - 2023 - Behavioral and Brain Sciences 46:e391.
    Although discriminative deep neural networks are currently dominant in cognitive modeling, we suggest that capacity-limited, generative models are a promising avenue for future work. Generative models tend to learn both local and global features of stimuli and, when properly constrained, can learn componential representations and response biases found in people's behaviors.
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  21.  14
    Outline of the Applicational Generative Model for the Description of Language.S. K. Šaumjan - 1965 - Foundations of Language 1 (3):189-222.
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  22. AI as Agency Without Intelligence: on ChatGPT, Large Language Models, and Other Generative Models.Luciano Floridi - 2023 - Philosophy and Technology 36 (1):1-7.
  23.  72
    Prediction, explanation, and the role of generative models in language processing.Thomas A. Farmer, Meredith Brown & Michael K. Tanenhaus - 2013 - Behavioral and Brain Sciences 36 (3):211-212.
    We propose, following Clark, that generative models also play a central role in the perception and interpretation of linguistic signals. The data explanation approach provides a rationale for the role of prediction in language processing and unifies a number of phenomena, including multiple-cue integration, adaptation effects, and cortical responses to violations of linguistic expectations.
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  24.  11
    Long-Range Correlation Underlying Childhood Language and Generative Models.Kumiko Tanaka-Ishii - 2018 - Frontiers in Psychology 9.
    Long-range correlation, a property of time series exhibiting long-term memory, is mainly studied in the statistical physics domain and has been reported to exist in natural language. Using a state-of-the-art method for such analysis, long-range correlation is first shown to occur in long CHILDES data sets. To understand why, Bayesian generative models of language, originally proposed in the cognitive scientific domain, are investigated. Among representative models, the Simon model was found to exhibit surprisingly good long-range correlation, but (...)
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  25. Discovering Binary Codes for Documents by Learning Deep Generative Models.Geoffrey Hinton & Ruslan Salakhutdinov - 2011 - Topics in Cognitive Science 3 (1):74-91.
    We describe a deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document. The top two layers of the generative model form an undirected associative memory and the remaining layers form a belief net with directed, top-down connections. We present efficient learning and inference procedures for this type of generative model and show that it allows more accurate and much faster (...)
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  26.  16
    A Bayesian generative model for learning semantic hierarchies.Roni Mittelman, Min Sun, Benjamin Kuipers & Silvio Savarese - 2014 - Frontiers in Psychology 5.
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  27. Generative Explanation and Individualism in Agent-Based Simulation.Caterina Marchionni & Petri Ylikoski - 2013 - Philosophy of the Social Sciences 43 (3):323-340.
    Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.
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  28.  73
    Scientific modelling in generative grammar and the dynamic turn in syntax.Ryan M. Nefdt - 2016 - Linguistics and Philosophy 39 (5):357-394.
    In this paper, I address the issue of scientific modelling in contemporary linguistics, focusing on the generative tradition. In so doing, I identify two common varieties of linguistic idealisation, which I call determination and isolation respectively. I argue that these distinct types of idealisation can both be described within the remit of Weisberg’s :639–659, 2007) minimalist idealisation strategy in the sciences. Following a line set by Blutner :27–35, 2011), I propose this minimalist idealisation analysis for a broad construal of (...)
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  29.  11
    Generative and Perceptive Models of Volition.Danil N. Razeev - 2021 - Epistemology and Philosophy of Science 58 (1):112-124.
    In recent decades, scientists and philosophers have developed several naturalistic theories of consciousness, in which they try to work out some theoretical foundations for a satisfactory solution to the problem of voluntary acts, in particular the genesis of voluntary bodily movements. From the author’s point of view, depending on which concept of consciousness scientists rely on in their empirical studies of voluntary movements, volition can be understood either as a generative act or as a perceptual act. The first part (...)
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  30.  8
    Investigation of a Word-Building System on the Basis of the Applicational Generative Model.P. A. Soboleva - 1970 - Semiotica 2 (1).
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  31.  12
    The Gravity of Objects: How Affectively Organized Generative Models Influence Perception and Social Behavior.Patrick Connolly - 2019 - Frontiers in Psychology 10.
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  32.  77
    Generative AI models should include detection mechanisms as a condition for public release.Alistair Knott, Dino Pedreschi, Raja Chatila, Tapabrata Chakraborti, Susan Leavy, Ricardo Baeza-Yates, David Eyers, Andrew Trotman, Paul D. Teal, Przemyslaw Biecek, Stuart Russell & Yoshua Bengio - 2023 - Ethics and Information Technology 25 (4):1-7.
    The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection (...)
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  33.  33
    A Generative Constituent-Context Model for Improved Grammar Induction.Dan Klein & Christopher D. Manning - unknown
    We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts. Parameter search with EM produces higher quality analyses than previously exhibited by unsupervised systems, giving the best published unsupervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher F1 of 71% on nontrivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to (...)
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  34.  10
    Relationships Among Job Burnout, Generativity Concern, and Subjective Well-Being: A Moderated Mediation Model.Xingniu Lan, Yinghao Liang, Guirong Wu & Haiying Ye - 2021 - Frontiers in Psychology 12:613767.
    Background:Policemen all over the world are tasked with the heavy work of maintaining social security. With the imbalance in mentality brought about by high population density and social transformation, the work of the Chinese police is particularly hard. As the window of demographic dividend is closing and the number of newborns is insufficient, China has started to adjust its established fertility policy to encourage a family to have two children. However, the results have not met the expectations of the policy (...)
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  35. Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition.Oren Kolodny, Arnon Lotem & Shimon Edelman - 2014 - Cognitive Science 38 (4):227-267.
    We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...)
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  36.  18
    Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition.Oren Kolodny, Arnon Lotem & Shimon Edelman - 2015 - Cognitive Science 39 (2):227-267.
    We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural‐language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...)
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  37.  14
    19 Generative Lexicon and the SIMPLE Model: Developing Semantic Resources for NLP.Federica Busa, Nicoletta Calzolari & Alessandro Lenci - 2001 - In Pierrette Bouillon & Federica Busa (eds.), The Language of Word Meaning. Cambridge University Press. pp. 333.
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  38.  23
    Generative and evolutionary models for design.John H. Frazer & Patrick Janssen - 2003 - Communication and Cognition: An Interdisciplinary Quarterly Journal 36 (3/4):187-215.
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  39.  19
    5 Generative process model building.Thomas J. Fararo - 2011 - In Pierre Demeulenaere (ed.), Analytical Sociology and Social Mechanisms. Cambridge University Press. pp. 99.
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  40.  25
    Generative and discriminative models of categorization.Deb Roy - 2005 - Trends in Cognitive Sciences 9 (8):389-396.
  41.  24
    Mechanisms and generative material models.Sim-Hui Tee - 2019 - Synthese 198 (7):6139-6157.
    Mechanisms consist of component parts and processes organized in a specific way to produce changes that may give rise to one or more phenomena. I aim to examine the generative mechanism of generative material models in the production of new material models. A generative material model in biology is a living material model that is capable of generating new material models. I contend that generative mechanisms of a generative material model are not (...)
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  42. Generative AI entails a credit–blame asymmetry.Sebastian Porsdam Mann, Brian D. Earp, Sven Nyholm, John Danaher, Nikolaj Møller, Hilary Bowman-Smart, Joshua Hatherley, Julian Koplin, Monika Plozza, Daniel Rodger, Peter V. Treit, Gregory Renard, John McMillan & Julian Savulescu - 2023 - Nature Machine Intelligence 5 (5):472-475.
    Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.
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  43.  29
    Linguistic Models in Narratology: From Structuralism to Generative Semantics.Marie-Laure Ryan - 1979 - Semiotica 28 (1-2).
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  44. Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity.Claudio Novelli, Federico Casolari, Philipp Hacker, Giorgio Spedicato & Luciano Floridi - manuscript
    The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper analyses the legal and regulatory implications of Generative AI and LLMs in the European Union context, focusing on liability, privacy, intellectual property, (...)
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  45. Modelling Empty Representations: The Case of Computational Models of Hallucination.Marcin Miłkowski - 2017 - In Gordana Dodig-Crnkovic & Raffaela Giovagnoli (eds.), Representation of Reality: Humans, Other Living Organism and Intelligent Machines. Heidelberg: Springer. pp. 17--32.
    I argue that there are no plausible non-representational explanations of episodes of hallucination. To make the discussion more specific, I focus on visual hallucinations in Charles Bonnet syndrome. I claim that the character of such hallucinatory experiences cannot be explained away non-representationally, for they cannot be taken as simple failures of cognizing or as failures of contact with external reality—such failures being the only genuinely non-representational explanations of hallucinations and cognitive errors in general. I briefly introduce a recent computational model (...)
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  46.  68
    Agent‐based computational models and generative social science.Joshua M. Epstein - 1999 - Complexity 4 (5):41-60.
  47.  23
    A generative transformational model for child language acquisition: A discussion of L. Bloom, language development: Form and function in emerging grammars.A. Schaerlaekens - 1973 - Cognition 2 (3):371-376.
  48. Generative Entrenchment and Evolution.Jeffrey C. Schank & William C. Wimsatt - 1986 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986:33 - 60.
    The generative entrenchment of an entity is a measure of how much of the generated structure or activity of a complex system depends upon the presence or activity of that entity. It is argued that entities with higher degrees of generative entrenchment are more conservative in evolutionary changes of such systems. A variety of models of complex structures incorporating the effects of generative entrenchment are presented and we demonstrate their relevance in analyzing and explaining a variety (...)
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  49.  43
    Generative AI and human–robot interaction: implications and future agenda for business, society and ethics.Bojan Obrenovic, Xiao Gu, Guoyu Wang, Danijela Godinic & Ilimdorjon Jakhongirov - forthcoming - AI and Society:1-14.
    The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study (...)
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  50.  30
    Learning Orthographic Structure With Sequential Generative Neural Networks.Alberto Testolin, Ivilin Stoianov, Alessandro Sperduti & Marco Zorzi - 2016 - Cognitive Science 40 (3):579-606.
    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can (...)
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