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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 6 Hits 1 – 20 of 115 1 Optimality Theory: Constraint Interaction in Generative Grammar ... Smolensky, Paul; Prince, Alan S.. - : Rutgers University, 2022 BASE Show details 2 Compositional processing emerges in neural networks solving math problems Russin, Jacob; Fernandez, Roland; Palangi, Hamid... In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 3 Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar McCoy, Richard Thomas; Culbertson, Jennifer; Smolensky, Paul... In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 4 Compositional Processing Emerges in Neural Networks Solving Math Problems ... Russin, Jacob; Fernandez, Roland; Palangi, Hamid. - : arXiv, 2021 BASE Show details 5 Distributed neural encoding of binding to thematic roles ... Lalisse, Matthias; Smolensky, Paul. - : arXiv, 2021 BASE Show details 6 Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar ... 43rd Annual Meeting of the Cognitive Science Society 2021; Culbertson, Jennifer; Legendre, Geraldine. - : Underline Science Inc., 2021 BASE Show details 7 Compositional processing emerges in neural networks solving math problems ... 43rd Annual Meeting of the Cognitive Science Society 2021; Fernandez, Roland; Gao, Jianfeng. - : Underline Science Inc., 2021 BASE Show details 8 How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN ... McCoy, R. Thomas; Smolensky, Paul; Linzen, Tal. - : arXiv, 2021 BASE Show details 9 Compositional Processing Emerges in Neural Networks Solving Math Problems Russin, Jacob; Fernandez, Roland; Palangi, Hamid... In: Cogsci (2021) BASE Show details 10 Emergent Gestural Scores in a Recurrent Neural Network Model of Vowel Harmony Smolensky, Paul; Smith, Caitlin; Rosen, Eric... In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 11 Testing for Grammatical Category Abstraction in Neural Language Models Kim, Najoung; Smolensky, Paul In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 12 Universal linguistic inductive biases via meta-learning ... McCoy, R. Thomas; Grant, Erin; Smolensky, Paul. - : arXiv, 2020 BASE Show details 13 Tensor Product Decomposition Networks: Uncovering Representations of Structure Learned by Neural Networks McCoy, Richard T; Linzen, Tal; Dunbar, Ewan... In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 14 Learning a gradient grammar of French liaison Smolensky, Paul; Rosen, Eric; Goldrick, Matthew In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2019 Annual Meeting on Phonology ; 2377-3324 (2020) BASE Show details 15 RNNs Implicitly Implement Tensor Product Representations Mccoy, R. Thomas; Linzen, Tal; Dunbar, Ewan... In: International Conference on Learning Representations ; ICLR 2019 - International Conference on Learning Representations ; https://hal.archives-ouvertes.fr/hal-02274498 ; ICLR 2019 - International Conference on Learning Representations, May 2019, New Orleans, United States (2019) BASE Show details 16 Quantum Language Processing ... Wiebe, Nathan; Bocharov, Alex; Smolensky, Paul; Troyer, Matthias; Svore, Krysta M. - : arXiv, 2019 Abstract: We present a representation for linguistic structure that we call a Fock-space representation, which allows us to embed problems in language processing into small quantum devices. We further develop a formalism for understanding both classical as well as quantum linguistic problems and phrase them both as a Harmony optimization problem that can be solved on a quantum computer which we show is related to classifying vectors using quantum Boltzmann machines. We further provide a new training method for learning quantum Harmony operators that describe a language. This also provides a new algorithm for training quantum Boltzmann machines that requires no approximations and works in the presence of hidden units. We additionally show that quantum language processing is BQP-complete, meaning that it is polynomially equivalent to the circuit model of quantum computing which implies that quantum language models are richer than classical models unless BPP=BQP. It also implies that, under certain circumstances, quantum ... Keyword: FOS Physical sciences; Quantum Physics quant-ph URL: https://arxiv.org/abs/1902.05162 https://dx.doi.org/10.48550/arxiv.1902.05162 BASE Hide details 17 Transient blend states and discrete agreement-driven errors in sentence production Goldrick, Matthew; Brehm, Laurel; Cho, Pyeong Whan... In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 18 Augmentic Compositional Models for Knowledge Base Completion Using Gradient Representations Lalisse, Matthias R; Smolensky, Paul In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 19 Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations ... Lalisse, Matthias; Smolensky, Paul. - : arXiv, 2018 BASE Show details 20 A Simple Recurrent Unit with Reduced Tensor Product Representations ... Tang, Shuai; Smolensky, Paul; de Sa, Virginia R.. - : arXiv, 2018 BASE Show details Page: 1 2 3 4 5 6

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