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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 Hits 1 – 20 of 32 1 The Zero Resource Speech Challenge 2021: Spoken language modelling Dunbar, Ewan; Bernard, Mathieu; Hamilakis, Nicolas... In: ISSN: 0162-8828 ; IEEE Transactions on Pattern Analysis and Machine Intelligence ; https://hal.inria.fr/hal-03329301 ; IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2021, pp.1-1. ⟨10.1109/TPAMI.2021.3083839⟩ (2021) BASE Show details 2 The Zero Resource Speech Challenge 2021: Spoken language modelling Dunbar, Ewan; Bernard, Mathieu; Hamilakis, Nicolas... In: Interspeech 2021 - Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-03329301 ; Interspeech 2021 - Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. ⟨10.1109/TPAMI.2021.3083839⟩ (2021) BASE Show details 3 Paraphrases do not explain word analogies ... Fournier, Louis; Dunbar, Ewan. - : arXiv, 2021 BASE Show details 4 The Zero Resource Speech Challenge 2021: Spoken language modelling ... Dunbar, Ewan; Bernard, Mathieu; Hamilakis, Nicolas. - : arXiv, 2021 BASE Show details 5 The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling Nguyen, Tu Anh; De Seyssel, Maureen; Rozé, Patricia... In: NeuRIPS Workshop on Self-Supervised Learning for Speech and Audio Processing ; https://hal.archives-ouvertes.fr/hal-03070362 ; NeuRIPS Workshop on Self-Supervised Learning for Speech and Audio Processing, Dec 2020, Virtuel, France (2020) BASE Show details 6 The Perceptimatic English Benchmark for Speech Perception Models Millet, Juliette; Dunbar, Ewan In: CogSci 2020 - 42nd Annual Virtual Meeting of the Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-03087248 ; CogSci 2020 - 42nd Annual Virtual Meeting of the Cognitive Science Society, Jul 2020, Toronto / Virtual, Canada (2020) BASE Show details 7 Perceptimatic: A human speech perception benchmark for unsupervised subword modelling Millet, Juliette; Dunbar, Ewan In: Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03087252 ; Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai / Virtual, China ; http://www.interspeech2020.org/ (2020) BASE Show details 8 Modelling Perceptual Effects of Phonology with ASR Systems Er Jiang, Bing,; Dunbar, Ewan; Sonderegger, Morgan... In: CogSci 2020 - 42nd Annual Virtual Meeting of the Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-03070281 ; CogSci 2020 - 42nd Annual Virtual Meeting of the Cognitive Science Society, Jul 2020, Virtual, France (2020) BASE Show details 9 The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units Dunbar, Ewan; Karadayi, Julien; Bernard, Mathieu... In: Interspeech 2020 - Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02962224 ; Interspeech 2020 - Conference of the International Speech Communication Association, Oct 2020, Shangai / Virtual, China (2020) BASE Show details 10 Analogies minus analogy test: measuring regularities in word embeddings Fournier, Louis; Dupoux, Emmanuel; Dunbar, Ewan In: CoNLL 2020 - 24th Conference on Computational Natural Language Learning ; https://hal.archives-ouvertes.fr/hal-03070260 ; CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Nov 2020, Virtual, France (2020) BASE Show details 11 Independent and Automatic Evaluation of Speaker-Independent Acoustic-to-Articulatory Reconstruction Parrot, Maud; Millet, Juliette; Dunbar, Ewan In: Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03087264 ; Interspeech 2020 - 21st Annual Conference of the International Speech Communication Association, Oct 2020, Shanghai / Virtual, China ; http://www.interspeech2020.org/ (2020) BASE Show details 12 Analogies minus analogy test: measuring regularities in word embeddings ... Fournier, Louis; Dupoux, Emmanuel; Dunbar, Ewan. - : arXiv, 2020 BASE Show details 13 The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling ... Nguyen, Tu Anh; de Seyssel, Maureen; Rozé, Patricia. - : arXiv, 2020 BASE Show details 14 Perceptimatic: A human speech perception benchmark for unsupervised subword modelling ... Millet, Juliette; Dunbar, Ewan. - : arXiv, 2020 BASE Show details 15 The Perceptimatic English Benchmark for Speech Perception Models ... Millet, Juliette; Dunbar, Ewan. - : arXiv, 2020 BASE Show details 16 Tensor Product Decomposition Networks: Uncovering Representations of Structure Learned by Neural Networks McCoy, Richard T; Linzen, Tal; Dunbar, Ewan; Smolensky, Paul In: Proceedings of the Society for Computation in Linguistics (2020) Abstract: We introduce an analysis technique for understanding compositional structure present in the vector representations used by neural networks. The inner workings of neural networks are notoriously difficult to understand, and in particular it is far from clear how they manage to perform remarkably well on tasks that depend on compositional structure even though they use continuous vector representations with no obvious compositional structure. Using our analysis technique, we show that the representations of these models can be closely approximated by Tensor Product Representations, a type of interpretable structure that lends significant insight into the workings of these hard-to-interpret models. Keyword: compositionality; Computational Linguistics; neural networks; symbolic structure; tensor product representations URL: https://scholarworks.umass.edu/scil/vol3/iss1/55 https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1130&context=scil BASE Hide details 17 Comparing unsupervised speech learning directly to human performance in speech perception Millet, Juliette; Jurov, Nika; Dunbar, Ewan In: Proceedings of the Annual Conference of the Cognitive Science Society (Cog Sci) ; CogSci 2019 - 41st Annual Meeting of Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-02274499 ; CogSci 2019 - 41st Annual Meeting of Cognitive Science Society, Jul 2019, Montréal, Canada (2019) BASE Show details 18 Generative grammar, neural networks, and the implementational mapping problem: Response to Pater Dunbar, Ewan In: ISSN: 0097-8507 ; EISSN: 1535-0665 ; Language ; https://hal.archives-ouvertes.fr/hal-02274522 ; Language, Linguistic Society of America, 2019, 95 (1), pp.e87-e98. ⟨10.1353/lan.2019.0013⟩ (2019) BASE Show details 19 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 20 The Zero Resource Speech Challenge 2019: TTS without T Dunbar, Ewan; Algayres, Robin; Karadayi, Julien... In: Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02274112 ; Interspeech 2019 - 20th Annual Conference of the International Speech Communication Association, Sep 2019, Graz, Austria (2019) BASE Show details Page: 1 2

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