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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; Rosen, Eric; Jojic, Nebojsa; Smolensky, Paul; Gao, Jianfeng In: Cogsci (2021) Abstract: A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition. Humans can infer the structured relationships (e.g., grammatical rules) implicit in their sensory observations (e.g., auditory speech), and use this knowledge to guide the composition of simpler meanings into complex wholes. Recent progress in artificial neural networks has shown that when large models are trained on enough linguistic data, grammatical structure emerges in their representations. We extend this work to the domain of mathematical reasoning, where it is possible to formulate precise hypotheses about how meanings (e.g., the quantities corresponding to numerals) should be composed according to structured rules (e.g., order of operations). Our work shows that neural networks are not only able to infer something about the structured relationships implicit in their training data, but can also deploy this knowledge to guide the composition of individual meanings into composite wholes. Keyword: Article URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491571/ http://www.ncbi.nlm.nih.gov/pubmed/34617074 BASE Hide 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. - : arXiv, 2019 BASE Show 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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