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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. - : 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) Abstract: Neural models of Knowledge Base data have typically employed compositional representations of graph objects: entity and relation embeddings are systematically combined to evaluate the truth of a candidate Knowedge Base entry. Using a model inspired by Harmonic Grammar, we propose to tokenize triplet embeddings by subjecting them to a process of optimization with respect to learned well-formedness conditions on Knowledge Base triplets. The resulting model, known as Gradient Graphs, leads to sizable improvements when implemented as a companion to compositional models. Also, we show that the "supracompositional" triplet token embeddings it produces have interpretable properties that prove helpful in performing inference on the resulting triplet representations. Keyword: Computational Linguistics; Computational Semantics; Knowledge Base Completion; Neural Networks URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1094&context=scil https://scholarworks.umass.edu/scil/vol2/iss1/27 BASE Hide 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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