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1
Cross-Situational Learning Towards Robot Grounding
In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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2
Cross-Situational Learning Towards Robot Grounding
In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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3
Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203318 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia (2021)
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4
Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
In: https://hal.inria.fr/hal-03203374 ; 2021 (2021)
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5
Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
In: https://hal.inria.fr/hal-03203318 ; 2021 (2021)
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6
Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203374 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia. pp.71--82, ⟨10.1007/978-3-030-86383-8_6⟩ ; https://link.springer.com/chapter/10.1007/978-3-030-86383-8_6 (2021)
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7
Cross-Situational Learning with Reservoir Computing for Language Acquisition Modelling
In: 2020 International Joint Conference on Neural Networks (IJCNN 2020) ; https://hal.inria.fr/hal-02594725 ; 2020 International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, Scotland, United Kingdom ; https://wcci2020.org/ (2020)
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8
Language Acquisition with Echo State Networks: Towards Unsupervised Learning
In: ICDL 2020 - IEEE International Conference on Development and Learning ; https://hal.inria.fr/hal-02926613 ; ICDL 2020 - IEEE International Conference on Development and Learning, Oct 2020, Valparaiso / Virtual, Chile (2020)
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9
Recurrent Neural Networks Models for Developmental Language Acquisition: Reservoirs Outperform LSTMs
In: SNL 2020 - 12th Annual Meeting of the Society for the Neurobiology of Language ; https://hal.inria.fr/hal-03146558 ; SNL 2020 - 12th Annual Meeting of the Society for the Neurobiology of Language, Oct 2020, Virtual Edition, Canada (2020)
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10
A Reservoir Model for Intra-Sentential Code-Switching Comprehension in French and English
In: CogSci'19 - 41st Annual Meeting of the Cognitive Science Society ; https://hal.inria.fr/hal-02432831 ; CogSci'19 - 41st Annual Meeting of the Cognitive Science Society, Jul 2019, Montréal, Canada ; https://cognitivesciencesociety.org/cogsci-2019/ (2019)
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11
An Empirical Study on Bidirectional Recurrent Neural Networks for Human Motion Recognition
Tanisaro, Pattreeya; Heidemann, Gunther. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2018. : LIPIcs - Leibniz International Proceedings in Informatics. 25th International Symposium on Temporal Representation and Reasoning (TIME 2018), 2018
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12
Teach Your Robot Your Language! Trainable Neural Parser for Modelling Human Sentence Processing: Examples for 15 Languages
In: https://hal.inria.fr/hal-01665807 ; 2017 (2017)
Abstract: We present a Recurrent Neural Network (RNN) that performs thematic role assignment and can be used for Human-Robot Interaction (HRI). The RNN is trained to map sentence structures to meanings (e.g. predicates). Previously, we have shown that the model is able to generalize on English and French corpora. In this study, we investigate its ability to adapt to various languages originating from Asia or Europe. We show that it can successfully learn to parse sentences related to home scenarios in fifteen languages: English, German, French, Spanish, Catalan, Basque, Portuguese, Italian, Bulgarian, Turkish, Persian, Hindi, Marathi, Malay and Mandarin Chinese. Moreover, in the corpora we have deliberately included variable complex sentences in order to explore the flexibility of the predicate-like output representations. This demonstrates that (1) the learning principle of our model is not limited to a particular language (or particular sentence structures), but more generic in nature, and (2) it can deal with various kind of representations (not only predicates), which enables users to adapt it to their own needs. As the model is inspired from neuroscience and language acquisition theories, this generic and language independent aspect makes it a good candidate for modelling human sentence processing. It is especially relevant when this model is implemented in grounded multimodal robotic architectures.
Keyword: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; [SCCO.LING]Cognitive science/Linguistics; Artificial neural networks; echo state networks; human-robot interaction; language learning; multilingual; natural language processing; recurrent neural network; reservoir computing; sentence processing
URL: https://hal.inria.fr/hal-01665807
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13
Recurrent Neural Network for Syntax Learning with Flexible Predicates for Robotic Architectures
In: The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB) ; https://hal.inria.fr/hal-01417697 ; The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB), Sep 2016, Cergy, France ; http://icdl-epirob.org/ (2016)
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14
Recurrent Neural Network Sentence Parser for Multiple Languages with Flexible Meaning Representations for Home Scenarios
In: IROS Workshop on Bio-inspired Social Robot Learning in Home Scenarios ; https://hal.inria.fr/hal-01417667 ; IROS Workshop on Bio-inspired Social Robot Learning in Home Scenarios, Oct 2016, Daejon, South Korea ; https://www.informatik.uni-hamburg.de/wtm/SocialRobotsWorkshop2016/index.php (2016)
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15
Echo State Networks For Arabic Phoneme Recognition ...
Hmad, Nadia; Allen, Tony. - : Zenodo, 2013
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16
Echo State Networks For Arabic Phoneme Recognition ...
Hmad, Nadia; Allen, Tony. - : Zenodo, 2013
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17
On-Line Processing of Grammatical Structure Using Reservoir Computing
In: In A. E. P. Villa, et al.: Artificial Neural Networks and Machine Learning - ICANN 2012 - 22nd International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-02561301 ; In A. E. P. Villa, et al.: Artificial Neural Networks and Machine Learning - ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Sep 2012, Lausanne, Switzerland. pp.596-603, ⟨10.1007/978-3-642-33269-2_75⟩ (2012)
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18
On-Line Processing of Grammatical Structure Using Reservoir Computing *
In: http://www.sbri.fr/files/publications/hinaut 12 icann.pdf
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19
2007 Special Issue Learning grammatical structure with Echo State Networks
In: http://cs.ucsd.edu/%7Eechristiansen/papers/grammar_esn.pdf
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