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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; Smolensky, Paul 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) Abstract: Accepted to ICLR 2019 ; International audience ; Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures into tensor product representations (TPRs; Smolensky, 1990), which additively combine tensor products of vectors representing roles (e.g., sequence positions) and vectors representing fillers (e.g., particular words). To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations. We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations, suggesting that these representations exhibit interpretable compositional structure; we explore the settings that lead RNNs to induce such structure-sensitive representations. By contrast, further TPDN experiments show that the representations of four models trained to encode naturally-occurring sentences can be largely approximated with a bag of words, with only marginal improvements from more sophisticated structures. We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated by TPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural representations. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] URL: https://hal.archives-ouvertes.fr/hal-02274498 BASE Hide 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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