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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
What does the Canary Say? Low-Dimensional GAN Applied to Birdsong
In: https://hal.inria.fr/hal-03244723 ; 2021 (2021)
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4
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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5
Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
In: https://hal.inria.fr/hal-03203374 ; 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
Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021)
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8
Editorial: Language and Robotics
In: ISSN: 2296-9144 ; Frontiers in Robotics and AI ; https://hal.inria.fr/hal-03533733 ; Frontiers in Robotics and AI, Frontiers Media S.A., 2021, 8, ⟨10.3389/frobt.2021.674832⟩ (2021)
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9
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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10
Hierarchical-Task Reservoir for Anytime POS Tagging from Continuous Speech
In: 2020 International Joint Conference on Neural Networks (IJCNN 2020) ; https://hal.inria.fr/hal-02594495 ; 2020 International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, Scotland, United Kingdom ; https://wcci2020.org/ (2020)
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11
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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12
A Journey in ESN and LSTM Visualisations on a Language Task
In: https://hal.inria.fr/hal-03030248 ; 2020 (2020)
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13
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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14
Learning to Parse Grounded Language using Reservoir Computing
In: ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics ; https://hal.inria.fr/hal-02422157 ; ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics, Aug 2019, Olso, Norway. ⟨10.1109/devlrn.2019.8850718⟩ ; https://ieeexplore.ieee.org/abstract/document/8850718 (2019)
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15
Teach Your Robot Your Language! Trainable Neural Parser for Modelling Human Sentence Processing: Examples for 15 Languages
In: ISSN: 2379-8920 ; EISSN: 2379-8939 ; IEEE Transactions on Cognitive and Developmental Systems ; https://hal.inria.fr/hal-01964541 ; IEEE Transactions on Cognitive and Developmental Systems, Institute of Electrical and Electronics Engineers, Inc, 2019, ⟨10.1109/TCDS.2019.2957006⟩ ; https://doi.org/10.1109/tcds.2019.2957006 (2019)
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16
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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17
Replication of Laje & Mindlin's model producing synthetic syllables
In: European Birdsong Meeting ; https://hal.inria.fr/hal-01964522 ; European Birdsong Meeting, Apr 2018, Odense, Denmark. 2018 (2018)
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18
Which Input Abstraction is Better for a Robot Syntax Acquisition Model? Phonemes, Words or Grammatical Constructions?
In: 2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) ; https://hal.inria.fr/hal-01889919 ; 2018 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Sep 2018, Tokyo, Japan (2018)
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19
From Phonemes to Sentence Comprehension: A Neurocomputational Model of Sentence Processing for Robots
In: SBDM2018 Satellite-Workshop on interfaces between Robotics, Artificial Intelligence and Neuroscience ; https://hal.inria.fr/hal-01964524 ; SBDM2018 Satellite-Workshop on interfaces between Robotics, Artificial Intelligence and Neuroscience, May 2018, Paris, France (2018)
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20
Neural coding of variable song structure in the songbird
In: EBM 2017 - European Birdsong Meeting ; https://hal.inria.fr/hal-01665824 ; EBM 2017 - European Birdsong Meeting, May 2017, Bordeaux, France. pp.1 ; https://birdsong2017.sciencesconf.org/ (2017)
Abstract: International audience ; Songbirds are an excellent model for exploring the neural coding of variable sequences ofcategorical acoustic elements. The domesticated canary, for instance, produce higlyvariable songs with complex transition rules between two consecutive acoustic elements.These transition rules are non-Markovian (i.e. the next acoustic element to be sung isdependent on several previous elements, not only the last one) [1].In the HVC of Bengalese finches, Bouchard and Brainard [5] found that variations inresponses to a given syllable could be explained by a positive linear dependence on theconvergence probability of preceding sequences.Here, we reanalyse data from [3] to see if similar findings could be found for canaries.
Keyword: [SCCO.NEUR]Cognitive science/Neuroscience; [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology
URL: https://hal.inria.fr/hal-01665824/document
https://hal.inria.fr/hal-01665824/file/poster17%20%281%29.pdf
https://hal.inria.fr/hal-01665824
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